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About the TeamThe Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve.We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste.Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use.About the RoleAs a member of Agent Post-Training, Artifacts, you will train frontier models to create polished, useful work products: documents, spreadsheets, slide decks, dashboards, reports, analyses, and other interactive or editable artifacts. You will help teach our models to move from a vague user goal to a finished artifact with strong structure, visual taste, domain judgment, correctness, and low latency. This work will require owning improvements across our post-training stack, including RL, data pipelines, graders, reward signals, evals, and behavioral analysis.You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models.In this role, you will:Design and run experiments that improve agentic model behavior for complex software and plugins..Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions.Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements.Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.You might thrive in this role if you:Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before.Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.Have some prior background in consulting, finance, marketing, operations, or data science.Compensation Range: $250K - $380K USDAbout OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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About the TeamThe Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve.We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste.Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use.About the RoleAs a member of Agent Post-Training, Connectors, you will teach models how to interface with the top professional software using code. You will help train agents to use code, APIs, tools, and structured integrations to operate across applications like Slack, Google Workspace, GitHub, Notion, Linear, Salesforce, and other core systems of work. You will help enable models to take useful actions across a user’s digital context: finding information, updating systems, coordinating work, generating artifacts, and completing multi-step workflows through the tools teams already use.You will train models to be supercharged by the world’s most important productivity and enterprise software, turning connected tools into a powerful action surface for our agents. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models.In this role, you will:Design and run experiments that improve agentic model behavior for complex software and plugins.Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions.Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements.Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.You might thrive in this role if you:Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before.Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.Compensation Ranges: $250K - $380K USDAbout OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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About the TeamThe Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve.We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste.Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use.About the RoleAs a member of Agent Post-Training, Computer Use, you will teach models to operate computers. You will help train models that can navigate browsers and desktops, use tools and applications, reason through complex workflows, collaborate with users and other agents, and complete long-horizon tasks with reliability and judgment. This work sits at the intersection of frontier model training, product behavior, evaluation, and systems engineering, and will directly shape the computer-use capabilities shipped in OpenAI’s next generation of agents. Currently, our models are the best in the world at this behavior!You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models.In this role, you mightDesign and run experiments that improve agentic model behavior for complex computer use, including desktop and browser.Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions.Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements.Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.You might thrive in this role if youHave strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before.Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.About OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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About the TeamThe Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve.We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste.Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use.About the RoleWe believe that the final enabler for AGI is spending compute on context. As a Context Researcher on Agent Post-Training, you will scale compute spent on context. You will get to work in our frontier training stack on enabling the next paradigm of model training with a clear product interface for iterative deployment (Codex Chronicle). You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models.In this role, you will:Design and run experiments that improve scaling of compute on context.Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions.Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements.Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.You might thrive in this role if you:Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before.Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.Compensation Range: $250K - $380K USDAbout OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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Applied AI EngineerEveryone's talking about AI. But here's the truth: ChatGPT can't send your emails. It can't book your flights. It can't even order you lunch.Why? Because AI is trapped in a chat box. It can't take real actions in the real world.We are changing that forever. We're not just building another AI company - we're creating the infrastructure that will power every AI application you'll use in the future.The Revolution Needs YouEvery AI app needs agentic "tools" - special functions that let AI models take real actions. Without tools, AI can only chat. With tools, AI can actually do things. We're building the actions runtime that allows AI agents to safely take real-world actions at enterprise scale. As an Applied AI Engineer on the Tools team, you'll push the boundary of what "a tool" even means at Arcade — designing agentic tools that go beyond deterministic API wrappers, building agents that build new tools, and composing tools into workflows that solve higher-level problems.Why This Is The Opportunity of a LifetimeFounder-Market Fit : Our CEO previously founded Stormpath (acquired by Okta), where he created the first Authentication API for developers. He's done this before - and this time the market is 10x bigger. Our CTO led the vector database team at Redis, shipped 100+ LLM applications, and is a contributor to LangChain and LlamaIndex. He knows this space better than anyone.Dream Team : We've assembled authentication, integrations, distributed systems, and AI experts from Okta, Redis, Microsoft, Splunk, Ngrok, Google, Airbyte, Disney, and HPE who've built and founded multiple successful developer platforms.Perfect Timing : We're at the inflection point of AI adoption. The biggest problem isn't better models - it's connecting AI to real-world actions. That's us.Massive Market : We're building critical infrastructure for the biggest technological shift of our generation. Every AI app will need what we're building.Backed By The Best: Our investors have backed Databricks, Clickhouse, MongoDB, Perplexity, Cohere, ScaleAI, Confluent, Elastic, and Firebase. They see what we see - this is going to be huge.The ChallengeYou'll report to the Engineering Manager for Tools and Growth. The Tools team owns Arcade's tool catalog — thousands of tools across many services, growing faster than any human can review by hand. The next leap in agent quality lives inside this team's work, and you'll be the applied-AI seat that pushes it forward.Three real problems define the role.Agentic tools vs. deterministic tools. Most tools today are deterministic: call X API with Y arguments, get Z result. That model breaks down for entire classes of agent work — research a topic, summarize a thread, decide which of three accounts to act on. Agentic tools, the ones that internally reason, plan, or call models are the answer, but the design space is wide open. When is agentic better than deterministic? How do you make an agentic tool fast, reliable, and debuggable? You'll set the bar for what these look like at Arcade. Agents that build tools. The toolkit catalog is too big for hand-crafting to scale. We need agent harnesses that can take a vendor's API and produce a high-quality toolkit — design, code, eval, docs with a human in the loop only where the human is actually needed. There's early work on this already. You'll take it from a prototype into the production pipeline that produces the next thousand tools. Workflows that compose tools. Individual tools solve narrow problems. Real customer outcomes: "close the quarter," "triage the inbox," "stand up the integration" need many tools, chained, with the right control flow. We need to figure out what the right primitive looks like above the tool layer, and you'll lead that design. The most honest thing we can say about this work: most of the problems you'll be solving didn't exist three months ago. There's no prior art. There's no known solution. If that's the part of the job that makes you nervous, this isn't the right role. If that's the part that makes you lean in, it is. We do real experiments. We form hypotheses. We publish learnings. Research is part of the job. But the role is built around shipping. If you want to spend six months proving an idea in a notebook before anything reaches a customer, this isn't the right role. If you want to ship the experiment and the writeup in the same quarter, it is.What You'll DoDesign and ship agentic tools that go beyond deterministic API wrappers — and define the patterns the rest of the Tools team will use to build more.Build the agent harness that automates tool creation — take a vendor's API, produce a high-quality toolkit end-to-end, keep humans in the loop only where humans add real value.Design workflows that compose tools into higher-level abstractions customers can actually point at outcomes ("triage this inbox," "close out this account") rather than individual API calls.Bring applied-ML rigor to tool design — evals, model-aware iteration, retrieval, tool description tuning, response shaping. Make decisions defensible with data.Run model-aware experiments across Claude, GPT, Gemini — agentic tool behavior diverges across models in ways nobody else is studying, and we should.Set the technical bar for what "good tool-building" looks like as the team scales — your patterns get inherited by every toolkit author after you.Contribute back to the MCP and agent ecosystem where the conversation about agentic tools is forming.Required Skills5+ years software engineering experience, with at least 2 years shipping production ML or applied-AI systems. Formal title matters less than the work.Strong Python.LLM application depth — prompting, retrieval, tool use, agent design. You've built non-trivial agent systems and know where the rough edges are.Experience designing or composing multi-tool / multi-agent workflows that produced real outcomes.You've built evals at scale — not "I ran a benchmark once," but a measurement system real engineering decisions were made against.Statistics fluency — significance, confidence intervals, A/B test design. You can defend whether a small delta is real or noise.Comfort across multiple frontier models and reasoning about their behavioral differences.A do-er, not a researcher-in-residence. You'd rather ship a working v0.5 next week than a polished v2.0 next quarter.Comfort with ambiguity — early team, narrow charter that will expand. You make good decisions with incomplete data.An insatiable desire to ship.Bonus PointsYou've built agents that build software (codegen agents, harness-style systems, meta-agents).Prior work on tool-use specifically — BFCL, τ-bench, ToolBench, MCP eval work, or equivalent.MCP ecosystem familiarity — extra bonus if you've filed an issue against the spec.You've worked on agent frameworks (LangChain, CrewAI, AutoGen, Mastra) and have opinions about where they get tool use and workflow composition wrong.Prior experience at an API platform, integrations-heavy product, or developer tools company.Join The MovementWe're not just building a product - we're leading a movement to transform AI from just chatbots to agents that can take actions against real systems. This is your chance to be at the forefront of that revolution.If you want to look back in 5 years and say, "I helped build that", then we want to talk to you. Ready to make AI actually useful? Apply NowCompensation and BenefitsThis role offers a competitive salary, equity, and benefits. Compensation is aligned with the range below and determined based on a candidate's background, experience, and performance.Salary Range $179,000-240,000 USD

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Senior Backend Engineer🌍 UK / Europe (Remote or Hybrid, it’s up to you!)💰 Dependent on experience📈 Be part of our success with the opportunity to join our equity scheme  🦸‍♀️ The Role 🦸‍♀️Our mission is to help large successful brands like Uber, Amazon, Wise, HelloFresh (and more!) put their customers at the centre of everything they do. Using best-in-class tech in a fast-evolving AI space, our Customer Experience Intelligence platform continuously analyses explicit and implicit feedback to help our clients identify what to do next.We're now looking for a talented Senior Backend Engineer to join the team! 👉 What you'll be doing:You’ll be designing, building and scaling the backend services and APIs that power Chattermill’s core analytics platform. Owning and driving the delivery of high-impact engineering workstreams and integrating AI capabilities into the product experience. This means you will:Design, build, and maintain scalable backend services and APIs that power Chattermill’s core analytics platform.Architect reliable, maintainable distributed systems and contribute to the evolution of backend service design and infrastructure.Own end-to-end delivery of backend engineering workstreams, from technical scoping and architecture through to implementation, testing, observability, and production support.Integrate language models, agentic frameworks, and AI pipelines into core product and backend services.Drive performance, reliability, and observability across high-throughput distributed data systems, including logging, tracing, alerting, and incident response.Work with cloud infrastructure and distributed systems in GCP (preferred) or AWS environments.Collaborate closely with Product to define scope, shape technical solutions, and explore new platform capabilities and features.Contribute to engineering excellence through code reviews, architectural discussions, and continuous improvement of development standards across the team. 🧰 What you’ll need: Strong professional experience operating as a senior backend engineer within high-ownership product engineering teams.Strong proficiency in at least one modern backend language such as Python, Ruby, or Go, with a focus on scalable system design and architectural thinking.Solid understanding of distributed systems, RESTful APIs, and event-driven architectures.Experience optimising database performance at scale, including query tuning, indexing, and partitioning across large production datasets.Cloud platform experience, ideally within GCP environments, although AWS experience is also valued.Hands-on experience with observability tooling and production operations, including logging, tracing, alerting, and incident response.Proven ability to independently deliver backend features and technical projects in fast-paced product environments.➕It would be a bonus if you:Experience integrating LLMs, prompt engineering techniques, or agentic frameworks such as Anthropic, OpenAI, or MCP.Familiarity with AI-assisted development workflows and tools such as Cursor, Copilot, or Claude Code.Experience working with ClickHouse, BigQuery, or other analytical databases supporting large-scale analytical workloads.Experience with customer feedback analytics, NLP, or text analytics platforms.Familiarity with Elasticsearch or large-scale search infrastructure.Experience building asynchronous or event-driven systems using technologies such as PubSub, Kafka, or equivalent.Contributions to open-source projects, conference speaking, or public technical writing. 🔎 Our Hiring ProcessLet’s introduce ourselves – you’ll complete an introductory asynchronous interview - we’d love to learn more about you, your ambitions, and what you’re looking for in your next step.Show us how your work and reason - You’ll meet with a member of our engineering team to complete a Live pseudo-code exercise.Get to know your would-be team – You'll meet people you'll be working closely with from our Engineering team, and complete a System design and architecture-based interview.Get to know your would-be manager – you’ll have a call with Ugo Anomelechi, our VP of Engineering, to learn more about the role and show off your experience.How our values and your career goals align – you’ll have a call with our cofounder to learn more about life at Chattermill and ensure we’re the right place for your next stage of growth.Our Perks 🤸🏽‍♂️ Flexibility & Work SetupFlexible working in a choice-first environment - we trust the way you want to work!Work-from-home allowance to set up your ideal workspace🌴 Time Off25 days holiday + local bank holidays, plus an extra day for each year of serviceYour birthday off🌱 Growth & OwnershipAnnual learning & development budget to support your growth (increasing over time)Equity options — share in the company’s success💚 Health & WellbeingMonthly health & wellbeing budget, increasing with length of serviceOptional private healthcare planLife assurance & income protection (location dependent)Employee Assistance Programme (location dependent) for extra support when you need itEnhanced family leave (location dependent), plus fertility and neonatal leave🌆 Office PerksIf you’re in London, a dog-friendly office with great classes, events, and a rooftop terrace💖 Our Values We are obsessed with experience – We take our mission to rid the world of bad Customer Experience seriously, and we practice what we preach.We believe in the power of trust – Whether it's with each other, our customers, partners, or other stakeholders, we always communicate with openness and trust.We act as responsible owners – Whether it's about the company, a team, a project, or a task, having the freedom to make decisions in our area of responsibility is a crucial driver for us.We share a passion for growth & progress – On every level, we’re motivated by taking on new challenges – even if they seem out of reach. We recognise that we are learning machines and we always seek to action feedback and improve collectively.We set our ambitions high but stay humble – We've come together to build a product and a category that’s never been seen before. While we're an ambitious bunch with lofty goals, we don't approach this goal carelessly.We believe the right team is the key to success – At Chattermill we’ve learned that all our important achievements have been the result of the right people collaborating together – that’s why we need you to apply today! 🌈 Diversity & Inclusion 🌈We want to enable exceptional experiences for everyone, and to achieve this we need everyone’s voice in our team.  We are on a mission to bring more diversity into the business and to give everyone (from all backgrounds and abilities) a chance to join us, even if they may not fit all of the requirements set out in this job spec. We realise that some may be hesitant to apply for a role when they don’t meet 100% of the listed requirements – we believe in potential and will happily consider all applications based on the skills and experience you have, we’d love to be part of your growth and we encourage you to apply!   #seniorbackendengineer #backenddeveloper #distributedsystems #cloudengineering #Python #GoLang #AIEngineering #LLM #Kafka #SaaS #AI #GCP #AWS #apidevelopment #engineering #engineer #developer #tech

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About Us Observe.AI is the AI Agents platform for customer experience, designed to help organizations deliver faster, smarter, and more efficient customer service at scale. The platform enables businesses to deploy specialized AI agents that autonomously execute work across the full CX lifecycle—from handling customer conversations to supporting frontline teams and optimizing operations. Each AI agent is purpose-built for a specific role, equipped to understand context, make decisions, take action, and continuously improve outcomes. This allows organizations to increase resolution speed, elevate service quality, and reduce operational costs while empowering your frontline team to focus on higher-value work. Built on a CX-native foundation, Observe.AI helps leading brands like DoorDash, Affordable Care, Signify Health, and Verida improve customer satisfaction, boost agent productivity, and deliver consistent, scalable performance across every customer interaction. Why Join Us We’re looking for an AI Agent Engineer to lead the charge in building and deploying enterprise-grade Voice, Chat AI agents and AI Copilot. This role is hands-on, customer-facing, and pivotal in bringing AI solutions to life - from design and integration to deployment and optimization. You’ll own the end-to-end lifecycle of AI agents: building, integrating, testing, demoing to clients, deploying into production, and tuning performance. What you’ll be doing Build & Deploy Agents: Own the implementation of AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Client Engagement: Act as the primary technical partner for customers—lead regular demos, communicate progress, gather feedback, and guide solutions from concept to production. Systems Integration: Configure and connect systems using APIs—handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. Telephony Integration: Set up SIP/CCaaS/PSTN routing, pass metadata, configure fallbacks, and troubleshoot call quality. Prompt Design & Optimization: Write and refine prompts for LLM-driven agents, monitor performance, test iteratively, and ensure agents meet automation and containment targets. Strategic Partner: Translate customer requirements into actionable solutions; work consultatively to unblock challenges in security, connectivity, or knowledge ingestion. Cross Functional Collaboration: Collaborate with product/engineering teams to escalate platform gaps and resolve deep technical fixes and platformization, while independently driving leading client implementations. What you’ll Bring Bachelor’s degree in Computer Science, Engineering, or a related technical field 3+ years in conversational AI, solution engineering, system integration, or delivering AI/LLM-based applications in customer environments, software engineering, or system integration with hands-on delivery of AI/LLM-based solutions. Strong ability to communicate and  lead customer-facing discussions - from deep technical troubleshooting to weekly project demos. Ability to explain complex technical concepts to non-technical audiences.  Must have strong hands-on skills in prompt design, workflow building and API integration (SIP, Twilio, Amazon Connect, etc.). Familiarity with LLMs (GPT, Claude, Gemini), vector DBs, and orchestration frameworks (LangChain, LlamaIndex, etc.). Working knowledge of retrieval-augmented generation (RAG) concepts, implementation patterns and performance optimization. Programming experience in Python, JavaScript, or similar for scripting and integrations Strong problem-solving mindset: ability to find workarounds, unblock integrations, and adapt to customer-specific ecosystems.  Experience with integration tools and Integration Platform-as-a-Service (iPaaS) providers, such as n8n, Zapier, or similar platforms and proficiency in API integrations and data flow management is a plus. Familiarity with telephony or voice systems (SIP, CCaaS, PSTN) is a plus.  Why You’ll Love It Here   Competitive compensation including equity: Market-aligned base pay, performance incentives, and meaningful equity ownership Excellent medical, dental, and vision insurance options: Comprehensive medical, dental and vision benefits for employees and eligible dependents Flexible Paid Time Off: Our unlimited, flexible PTO policy empowers you to take the time you need to recharge, maintain balance, and perform at your best. Additional Time to Recharge: 10 company holidays, an annual company-wide Winter Break, and paid parental leave to fully support life outside of work. 401(k) plan: Long-term financial planning support with tax-advantaged retirement savings Quarterly Lifestyle Spending Account: Flexible quarterly stipend to support wellness, learning and professional development, and personal growth Monthly Mobile + Internet Stipend: Support for remote and hybrid work connectivity needs Pre-tax Commuter Benefits: Tax-efficient transit and commuting support for hybrid and in-office employees Autonomy and Agency: Play a meaningful role in scaling a category-defining GenAI platform transforming the future of customer experience. Salary Range The base salary compensation range targeted for this full-time position is $108 - 170K per annum. Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives and equity (in the form of options). This salary range is an estimate, and the actual salary may vary based on the Company’s compensation practices. Our Commitment to Inclusion and Belonging Observe.AI is an Equal Employment Opportunity employer that proudly pursues and hires a diverse workforce. Observe AI does not make hiring or employment decisions on the basis of race, color, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender identity, sexual orientation, disability, age, military or veteran status, or any other basis protected by applicable local, state, or federal laws or prohibited by Company policy. Observe.AI also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. We welcome all people. We celebrate diversity of all kinds and are committed to creating an inclusive culture built on a foundation of respect for all individuals. We seek to hire, develop, and retain talented people from all backgrounds. Individuals from non-traditional backgrounds, historically marginalized or underrepresented groups are strongly encouraged to apply. If you are ambitious, make an impact wherever you go, and you're ready to shape the future of Observe.AI, we encourage you to apply. For more information, visit www.observe.ai.  #LI-Hybrid

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About Us Observe.AI is the AI Agents platform for customer experience, designed to help organizations deliver faster, smarter, and more efficient customer service at scale. The platform enables businesses to deploy specialized AI agents that autonomously execute work across the full CX lifecycle—from handling customer conversations to supporting frontline teams and optimizing operations. Each AI agent is purpose-built for a specific role, equipped to understand context, make decisions, take action, and continuously improve outcomes. This allows organizations to increase resolution speed, elevate service quality, and reduce operational costs while empowering your frontline team to focus on higher-value work. Built on a CX-native foundation, Observe.AI helps leading brands like DoorDash, Affordable Care, Signify Health, and Verida improve customer satisfaction, boost agent productivity, and deliver consistent, scalable performance across every customer interaction. Why Join Us We’re looking for an AI Agent Engineer to lead the charge in building and deploying enterprise-grade Voice, Chat AI agents and AI Copilot. This role is hands-on, customer-facing, and pivotal in bringing AI solutions to life - from design and integration to deployment and optimization. You’ll own the end-to-end lifecycle of AI agents: building, integrating, testing, demoing to clients, deploying into production, and tuning performance. What you’ll be doing Build & Deploy Agents: Own the implementation of AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Client Engagement: Act as the primary technical partner for customers—lead regular demos, communicate progress, gather feedback, and guide solutions from concept to production. Systems Integration: Configure and connect systems using APIs—handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. Telephony Integration: Set up SIP/CCaaS/PSTN routing, pass metadata, configure fallbacks, and troubleshoot call quality. Prompt Design & Optimization: Write and refine prompts for LLM-driven agents, monitor performance, test iteratively, and ensure agents meet automation and containment targets. Strategic Partner: Translate customer requirements into actionable solutions; work consultatively to unblock challenges in security, connectivity, or knowledge ingestion. Cross Functional Collaboration: Collaborate with product/engineering teams to escalate platform gaps and resolve deep technical fixes and platformization, while independently driving leading client implementations. What you’ll Bring Bachelor’s degree in Computer Science, Engineering, or a related technical field 3+ years in conversational AI, solution engineering, system integration, or delivering AI/LLM-based applications in customer environments, software engineering, or system integration with hands-on delivery of AI/LLM-based solutions. Strong ability to communicate and  lead customer-facing discussions - from deep technical troubleshooting to weekly project demos. Ability to explain complex technical concepts to non-technical audiences.  Must have strong hands-on skills in prompt design, workflow building and API integration (SIP, Twilio, Amazon Connect, etc.). Familiarity with LLMs (GPT, Claude, Gemini), vector DBs, and orchestration frameworks (LangChain, LlamaIndex, etc.). Working knowledge of retrieval-augmented generation (RAG) concepts, implementation patterns and performance optimization. Programming experience in Python, JavaScript, or similar for scripting and integrations Strong problem-solving mindset: ability to find workarounds, unblock integrations, and adapt to customer-specific ecosystems.  Experience with integration tools and Integration Platform-as-a-Service (iPaaS) providers, such as n8n, Zapier, or similar platforms and proficiency in API integrations and data flow management is a plus. Familiarity with telephony or voice systems (SIP, CCaaS, PSTN) is a plus.  Why You’ll Love It Here   Competitive compensation including equity: Market-aligned base pay, performance incentives, and meaningful equity ownership Excellent medical, dental, and vision insurance options: Comprehensive medical, dental and vision benefits for employees and eligible dependents Flexible Paid Time Off: Our unlimited, flexible PTO policy empowers you to take the time you need to recharge, maintain balance, and perform at your best. Additional Time to Recharge: 10 company holidays, an annual company-wide Winter Break, and paid parental leave to fully support life outside of work. 401(k) plan: Long-term financial planning support with tax-advantaged retirement savings Quarterly Lifestyle Spending Account: Flexible quarterly stipend to support wellness, learning and professional development, and personal growth Monthly Mobile + Internet Stipend: Support for remote and hybrid work connectivity needs Pre-tax Commuter Benefits: Tax-efficient transit and commuting support for hybrid and in-office employees Autonomy and Agency: Play a meaningful role in scaling a category-defining GenAI platform transforming the future of customer experience. Salary Range The base salary compensation range targeted for this full-time position is $108 - 170K per annum. Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives and equity (in the form of options). This salary range is an estimate, and the actual salary may vary based on the Company’s compensation practices. Our Commitment to Inclusion and Belonging Observe.AI is an Equal Employment Opportunity employer that proudly pursues and hires a diverse workforce. Observe AI does not make hiring or employment decisions on the basis of race, color, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender identity, sexual orientation, disability, age, military or veteran status, or any other basis protected by applicable local, state, or federal laws or prohibited by Company policy. Observe.AI also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. We welcome all people. We celebrate diversity of all kinds and are committed to creating an inclusive culture built on a foundation of respect for all individuals. We seek to hire, develop, and retain talented people from all backgrounds. Individuals from non-traditional backgrounds, historically marginalized or underrepresented groups are strongly encouraged to apply. If you are ambitious, make an impact wherever you go, and you're ready to shape the future of Observe.AI, we encourage you to apply. For more information, visit www.observe.ai.  #LI-Hybrid

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FORWARD DEPLOYED ENGINEER About H: H Company is a next-generation AI research and product company pioneering the future of autonomous, agentic AI. Founded to build intelligence that acts, H Company is creating the foundational infrastructure for autonomous AI systems that drive real-world outcomes across industries. About the Team: The Forward Deployed Team is committed to ensuring the successful implementation of our Agents to help our customer automate and optimize tasks, and allow them to focus on what really matters. The team is also responsible to shape the future vision of our products by distilling learnings from the field into the H platform. That platform empowers a wide range of professionals and businesses to safely, easily and autonomously deploy agents at scale for a broad range of tasks: from ensuring websites behave as expected to automating repeated tasks. Key Responsibilities:Understand and identify customer’s most important and impactful multi-step task to automateSeamlessly integrate with customer systems, including but not limited to databases, third-party APIs, and internal tools, etc.Rapidly iterate in the field with tight feedback loops to continuously improve product fit and drive end-to-end solution deliveryArchitect, build, and maintain production-grade services that scale with usage and complexityCollaborate closely with product, research, and engineering teams to shape the roadmap and align on priorities based on findings in the field Requirements:3+ years of professional software engineering experience, ideally in fast-paced environments (e.g., startups, consulting, or product teams), or otherwise proven technical acumen through projects and past experiencesExperience working with AI/ML systems, especially integrating LLMs or other agentic workflows into real productsFluency in at least one backend language (e.g., Python, Go, Node.js, Java) in addition to English ;-)Familiarity with frontend frameworks (e.g., React, Vue) and a good sense of UI/UX tradeoffsExperience building and deploying production systems—APIs, services, infrastructure, and/or data pipelinesProficiency in integrating third-party APIs, databases, and external systems in a secure and reliable wayStrong problem-solving skills and ability to thrive in ambiguous, customer-facing environmentsExcellent communication skills, both written and verbalBias for action—able to ship quickly, iterate fast, and own the full lifecycle of solutionsWillingness to travel for on-site deployments or customer engagements Location:H's teams are distributed throughout France, the UK, and the USThis role will be based either in Paris, France or Remote, USThe final decision for this will lie with the hiring manager for each individual role What We Offer:Be part of the founding journey of one of the most exciting AI startups shaping the future of AI and agentic systems.Collaborate with a fun, dynamic and multicultural team, working alongside world-class AI engineers, researchers, and builders in a high-trust, high-impact environment.Enjoy a competitive compensation package, including salary and equityUnlock opportunities for professional growth, continuous learning, and career development

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Role Overview Scale’s rapidly growing Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world. Our core work consists of: Creating custom AI applications that will impact millions of citizens Generating high-quality training data for national LLMs Upskilling and advisory services to spread the impact of AI As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure required for our international government partners. At Scale, we’re not just building AI solutions—we’re enabling the public sector to transform their operations and better serve citizens through cutting-edge technology. If you’re ready to shape the future of AI in the public sector and be a founding member of our team, we’d love to hear from you. You will: Own the production outcome: Take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. Ensure Full-Stack integrity: Oversee the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment. Scale the feedback loop: Build automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring the right levels of reliability. Navigate global compliance: Manage the technical lifecycle within diverse regulatory frameworks. Incident command: Lead the response for production issues in mission-critical environments, ensuring rapid resolution and building the guardrails to prevent them from happening again. Bridge the gap: Translate deep technical performance metrics into clear insights for senior international government officials. Drive product evolution: Partner with our Engineering and ML teams to ensure the lessons learned in the field directly influence the technical architecture and decisions of future use cases. Ideally, you have: Experience: 6+ years in a high-impact technical role (SRE, FDE or MLOps) with experience in the public sector. Global perspective: Familiarity with international government security standards and the complexities of deploying sovereign AI. System architecture proficiency: Proven experience maintaining production-grade applications with a deep understanding of the full request lifecycle-connecting frontend/API layers to the backend and AI core. Modern AI Stack expertise: Proficiency in coding and the modern AI infrastructure, including Kubernetes, vector databases, agentic development, and LLM observability tools. Ownership: You treat every production deployment as your own. You race toward solving hard problems before the customer even sees them. Reliability: You understand that in the public sector, a model failure may be a risk to public safety or privacy. Customer communication: The ability to explain to a high-ranking official why the performance of the system has degraded and how we are fixing it. PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.  We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information. We comply with the United States Department of Labor's Pay Transparency provision.  PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

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About UsAt Hiya, we're making calls safe, useful, and human again.Voice is the most human form of communication, yet it's become one of the least trusted. Spam, scams, and AI manipulation have eroded what was once a simple way to connect. Hiya is changing that.Each month our AI voice technology analyzes 28+ billion calls, protecting over 550 million users and 800+ businesses worldwide. Partnering with a growing global network including, AT&T, Samsung, British Telecom EE, Rogers, MasOrange,Bell Canada, MasMovil, and Virgin Media O2, we're not just stopping bad actors, we're helping people feel good and confident about picking up the phone again.This is a pivotal moment for voice. As new threats and technologies accelerate, so does demand for trusted voice communication. Hiya is growing 40%+ year over year, expanding globally, and defining what voice becomes next.Join us! You won't just work on what voice is today, you'll shape what it becomes tomorrow: smarter, safer, and genuinely worth answering again.About the RoleHiya is hiring a Senior Software Engineer to join the Protect team, responsible for building and scaling the AI-powered spam and scam detection systems that protect hundreds of millions of people from fraudulent calls before they ring. As voice threats evolve faster than ever; driven by AI-generated abuse, sophisticated spoofing, and increasingly complex attack patterns. You'll own the end-to-end pipeline that enables our data science team to develop increasingly sophisticated scam detection models while maintaining strict privacy and regulatory compliance. It's all about building the pipelines that allow Hiya to stay ahead of rapidly evolving voice threats and deliver measurably better protection to mobile operators and their subscribers worldwide. You'll shape how protection scales across our network, how quickly we adapt to new threats, and how effectively we translate AI innovation into real-world safety outcomes.What You'll DoOwn the complete development lifecycle for spam and scam detection infrastructure - from researching and proposing solutions to implementation, testing, deployment, production maintenance, and monitoringParticipate in on-call rotation, ensuring rapid recognition and resolution of production issues while continuously improving system reliabilityDesign and build frameworks that enable data scientists to develop, test, and deploy increasingly complex scam detection models with access to call data in a privacy-aware, regulation-compliant mannerMake independent implementation decisions while driving collaborative design discussions that improve system quality, long-term maintainability, and cost-effectiveness across the teamEvaluate critical tradeoffs between immediate fixes and durable solutions when production issues arise, prioritizing overall service quality and system resilienceCollaborate proactively with cross-functional partners - including product managers, data scientists, and other engineering teams - to align technical decisions with business impact, user needs, and Hiya's broader strategic prioritiesRecognize and evangelize engineering patterns, design principles and architectural decisions that could be adopted across teams to raise overall quality and execution speedInfluence how the team operates by pushing back on solutions that don't align with design principles, surfacing issues early during project planning, and reasoning clearly about business impact versus costWhat We’re Looking ForRequired4+ years of software engineering experience, with strong expertise in distributed systems, microservices, network architecture, and database systemsProven experience operating distributed systems in production environmentsAbility to quickly recognize, diagnose and resolve production issuesRelevant technical experience may include:RxM engineeringTCP/IPDNS resolutionWeb backend developmentAbility to evaluate tradeoffs between immediate fixes and long-term solutionsStrong focus on service quality, reliability and system resilienceAbility to work independently on implementation while knowing when to involve others in design discussionsConfidence making well-informed technical decisions and clearly explaining tradeoffsInterest in agentic workflows, including engineering automation and customer-facing applicationsPreferred6+ years of software engineering experienceExperience building frameworks or infrastructure that support data science or machine learning workflowsFunctional programming experienceExperience with data processing pipelines, especially Kafka or similar technologiesFamiliarity with client-side processing pipelinesDevOps, monitoring, and observability experienceFamiliarity with on-call rotationThe person in this role must embody Hiya’s key values of Serving our customers, Doing rather than observing, Improving ourselves and our business, Owning and holding ourselves accountable for success, and Leading by showing up with a point of view, engaging in open discussion, listening respectfully to others opinions and committing to decisions.The requirements listed in the job descriptions are guidelines. You don’t have to satisfy every requirement or meet every qualification listed. If your skills are transferable we would still love to hear from you.More DetailsWhen determining compensation, a number of factors will be considered: skills, experience, job scope, location and competitive compensation market data.BenefitsStock optionsPrivate Retirement/Pension program through Erste BankGenerali Private Health CareDonation Matching for a charity of your choice (up to $500/ year)WFH equipment stipend (up to $500 in your first year)$1,000/year in Professional Development fundsOnline English ClassesGym membershipFlexible working model (2 days a week, Tuesdays and Thursday is required in the office. In the rest of the month you decide whether you are working from home or from the office.)This position is based in Budapest, Hungary.We are building a team with a variety of perspectives, identities, and professional experiences. We evaluate great candidates through a business lens and we strongly believe that diversity and unique perspectives make our company stronger, more dynamic, and a great place to build a career.Our team has won various awards over the last 4 years from Built-in Seattle and Seattle Business Week to #86 on Deloitte Technology Fast 500 and Forbes #1 Startup Employer. Here at Hiya, we are a people-centric company focused on helping each and every one of our employees grow both personally and professionally. We feel that creating a team culture of support and empowerment to challenge the status quo results in an energized and passionate team that is continuously challenged and passionate about the work they are doing. You'll love working here if you are looking for an innovative challenge that is disrupting an industry. Come join us!

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Why ClariumThe healthcare industry overspends on its supply chain by over $25B each year — the result of fragmented data, inefficient workflows, and wasted supplies. Clarium is fixing that. Our AI-powered platform, Astra OS, gives hospitals end-to-end visibility into their supply chain operations, automating workflows and surfacing actionable insights so supply chain teams can focus on what matters most: patient care. We're trusted by some of the world's leading health systems, including Yale New Haven Health, Stanford, Geisinger, Cleveland Clinic, and Kaiser Permanente.Founded in 2020, Clarium has raised $43M in total funding. Our Series A was led by Northzone, with participation from General Catalyst, AlleyCorp, Kaiser Permanente Ventures, Texas Medical Center Ventures, and 1984 Ventures.The OpportunityClarium builds computer vision pipelines that extract structured data from clinical images under real-world conditions. This role owns the end-to-end pipeline: object detection, identification, reconciliation, and data extraction from images captured under variable lighting, camera angles, and workflow conditions with zero tolerance for errors.You’ll design and build production-ready CV pipelines that combine state-of-the-art object detection models, multimodal LLM/LVM APIs, and barcode/label decoding to produce structured, auditable inventory data that clinical and supply chain workflows depend on. This has direct implications for patient safety, billing accuracy, and supply chain optimization.In This Role You WillDesign, build, implement and optimize multi-stage CV pipelines spanning segmentation, object detection, multimodal LLM/LVM extraction, machine-readable code decoding, and multi-source reconciliationTrain or fine-tune detection models on custom medical supply datasetsBuild and own dataset strategy - leverage augmentation and synthetic data generation to improve the training and testing datasets when data doesn’t exist.Monitor and improve pipeline accuracy — instrument field-level metrics, diagnose failure modes, and systematically improve precision/recall through model iteration and preprocessing optimizationDesign persistence schemas and audit data models that make every extraction independently reviewableMaintain and extend the async Python backend services that surface pipeline results to downstream clinical workflowsWhat You’ll BringRequired:5+ years experience in computer vision and object detectionHands-on experience training and fine-tuning detection modelsExperience building OCR pipelines for label/packaging text extractionStrong Python skills with experience in OpenCV, image preprocessing, and augmentation techniquesProduction experience with multimodal LLM APIs for structured data extraction and validationBackend engineering: FastAPI, Pydantic v2, PostgreSQL, async PythonNice to Have:OCR pipelines for label/packaging text extractionExperience with barcode/QR/UDI decoding and preprocessing strategies that improve decode ratesMLOps experience: Docker, CI/CD, model versioning, A/B testingWorkflow orchestration tools (Temporal, Prefect, Airflow)Healthcare or supply chain domain experienceFamiliarity with medical device identification standards (UDI, GS1)Skills & Tools You'll Use:Need to Know: Python, PyTorch FastAPI · Pydantic v2 · PostgreSQL · Multimodal LLM APIs · Image preprocessing · Barcode / QR decoding, OCR pipelinesNice to Know: Zero-shot object detection · Temporal · Prefect · AirflowWhat You Get at ClariumIncentive Stock Options proportionate to your salaryFully remote — we're a distributed team across multiple time zonesUnlimited PTOTop-tier health, vision, and dental benefits401KThe opportunity to build on a strong foundational team with deep data and engineering roots at a stage where your work genuinely shapes the product

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Computer Vision Intern — Data Labeling & Annotation Type: Internship / Temporary Duration: 6 months - 12 months What You'll Gain Exposure to the full CV pipeline, from raw data to deployed model Mentorship from CV engineers working on production systems Hands-on experience with YOLO, PyTorch, and modern annotation workflows Concrete portfolio work — datasets, scripts, and model contributions — that translates directly to future ML/CV roles What You'll Do Annotate images and video for object detection (bounding boxes), segmentation (polygons/masks), and classification Help refine labeling schemas and class taxonomies as edge cases come up Write Python scripts to convert between annotation formats, validate label integrity, and generate dataset statistics QA labels and surface systematic errors or ambiguous cases Run baseline YOLO training experiments to evaluate dataset quality and identify labeling gaps Document conventions and edge-case decisions Required Pursuing a degree in CS, EE, AI/ML, or related field Working knowledge of Python and common CV libraries (NumPy, OpenCV) Attention to detail and patience for precision work Nice to Have Hands-on experience with YOLO Familiarity with PyTorch, segmentation masks, or model-assisted labeling workflows

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About Us:AltaML is a leading North American applied AI company with extensive experience in building and operationalizing AI software solutions. We are a company like no other – we believe in making small bets, failing fast, and being better together. We are looking for creative problem-solvers who obsess about the customer to find wins across different industries. We don’t hire for fit; we hire to add. We are looking for people who play our core values of being: Agile, Gritty Humble, and Happy. If you’re passionate about AI/ML, thrive in a dynamic environment, and want to work with a diverse team of wickedly smart people, we want to hear from you!We are looking for a Full Stack Software Engineer who builds software in an AI-native way — someone who treats Claude and the latest agentic coding tools as a core part of their craft, not a novelty. In this role, you will contribute to the technical delivery of ML-powered applications across cloud services, APIs, and modern front-end frameworks, with Claude Code, the Claude API, and agentic workflows woven into how you design, build, and ship. You will be an active contributor within your project pod, shipping features end-to-end, participating in technical design discussions, and growing your ability to translate business requirements into well-engineered solutions. You will take ownership of your work — writing clean, reviewable code, contributing to shared internal frameworks, and continuously developing your fluency with AI-assisted development. You will thrive in this role if you are a builder who leans on Claude Code to move fast without cutting corners. You write clear specs, review AI-generated code critically, and know when to delegate to an agent versus when to handcraft. You are curious about where LLMs fit (and where they don’t), and you bring a practical, evidence-based instinct to that question.What You'll Do:Full Stack Feature Delivery Implement features end-to-end across front-end, back-end, and cloud infrastructure layers, taking ownership from design through deployment Build and integrate RESTful APIs and cloud-hosted services, primarily on Azure, following established architecture patterns and security standards Develop front-end components using modern JavaScript/TypeScript frameworks, with attention to usability, performance, and maintainability Write unit, integration, and API tests as a standard part of delivery — not an afterthought — using frameworks appropriate to the stack (xUnit, Pytest, Postman, or similar) Use Docker for local development, environment parity, and containerized deployments Manage work in Git with clean branching, meaningful commit history, and effective collaboration with AI agents in the same workflow LLM Feature Development Build features that incorporate LLM calls via the Claude API or Azure OpenAI, including prompt design, context management, response handling, and cost-aware API usage Implement RAG components and tool integrations as part of product features, working within established architecture patterns and contributing to their evolution Write evaluation harnesses for LLM-powered features: regression tests for prompt behaviour, output quality checks, and agent tool use validation Document LLM feature behaviour clearly: what the system does, what it does not do, known failure modes, and the guardrails in place Develop growing awareness of when LLM-in-the-loop is the right architecture decision versus a conventional software approach — and contribute that perspective in design discussions Technical Design & Problem-Solving Participate actively in epic-level and feature-level design discussions, contributing well-reasoned proposals backed by research or prototype evidence Use Claude to accelerate technical research: explore design alternatives, evaluate libraries, and investigate unfamiliar domains quickly — then synthesize findings into a clear recommendation Identify and flag technical risks within your work scope early, with enough supporting detail for the tech lead or architect to make an informed decision Produce clear technical documentation: decision records, implementation notes, and design summaries that a future team member can act on AI-Native Development Use Claude Code and AI-assisted development tools (Cursor, GitHub Copilot, and similar) as a standard part of the engineering workflow — for prototyping, code generation, refactoring, documentation, and debugging Write clear, well-structured prompts and development specs that enable AI agents to produce useful, reviewable output — not vague instructions that generate noise Review AI-generated code with the same rigour as human-authored code: check for correctness, edge cases, security issues, and maintainability before merging Develop growing fluency in agentic development patterns: structuring repos for agent navigation, decomposing tasks into agent-friendly units, and knowing when human authorship is the right call Contribute to internal discussions on AI tooling effectiveness — share what is working, what isn’t, and help refine the team’s approach Collaboration & Growth Participate in code reviews constructively — give specific, actionable feedback and incorporate peer feedback into your own work without defensiveness Collaborate closely with ML engineers, data engineers, and product managers within the pod, understanding adjacent work well enough to minimize integration friction Contribute reusable components, utilities, and internal skills to AltaML’s shared libraries Engage in sprint ceremonies, stand-ups, and retrospectives as an active team member — raise blockers early, communicate progress clearly, and contribute to continuous improvement Proactively seek feedback from peers and tech leads to accelerate your own growth toward senior-level ownership and technical leadership What You Bring: Degree or equivalent work experience in Computer Science, Software Engineering, or a related technical discipline 3–5 years of professional full stack development experience, with a track record of shipping production features end-to-end Hands-on, daily-driver experience using Claude (Claude Code, claude.ai, or the Claude API), Cursor, or GitHub Copilot for real software engineering work — not just occasional use Strong working experience with cloud services, ideally Azure (Functions, App Service, Blob Storage, Azure OpenAI, or similar) Proficiency in a modern object-oriented language — C#, Python, TypeScript, or equivalent — with a clear point of view on writing clean, maintainable code Experience building and consuming RESTful APIs and integrating third-party services Solid front-end experience with a modern JavaScript/TypeScript framework (React, Vue, Angular, or similar) Experience writing unit and API tests as a standard part of delivery (xUnit, Pytest, Postman, or similar) Comfortable with Docker for local development and containerized deployments Proficiency with Git, including working effectively in a branch-based workflow alongside AI agents Experience working in an Agile environment with iterative delivery cycles Strong written and verbal communication skills — able to articulate technical decisions clearly to peers and participate confidently in client-facing discussions   Nice to Have's: Experience integrating LLM APIs (Claude, OpenAI, Azure OpenAI) into product features, including prompt design and cost management Exposure to RAG architectures, vector databases, or tool-augmented LLM workflows Familiarity with agentic frameworks (LangChain, LangGraph, Autogen, or similar) Experience writing evaluation harnesses or regression tests for LLM-powered features Exposure to CI/CD pipelines and automated deployment workflows (Azure DevOps, GitHub Actions, or similar) Prior experience in a consulting, applied AI, or client-delivery environment Contributions to open-source projects or internal platforms 90,000 - 110,000 a yearResponsible AI (RAI)AltaML employees, contractors, and associates must be trained and well-versed in the importance of Responsible AI and empowered to enact RAI principles by developing and deploying AI solutions. They should also be empowered to raise and escalate RAI concerns as required. AltaML is responsible for elevating public discourse and awareness of AI through open, transparent communications with the broader public. Equal OpportunitiesAltaML is dedicated to fostering a safe, diverse, and inclusive workplace as an equal-opportunity employer. We welcome applications from qualified individuals of all backgrounds, encompassing ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, nationality, and educational backgrounds. If you are invited for an interview and require accommodations during the interview process, please don’t hesitate to let us know. AltaML acknowledges that its head office is located on Treaty 6 territory, and respects the histories, languages, and cultures of First Nations, Métis, Inuit, and all First Peoples of Canada, whose presence continues to enrich our vibrant community. We Look for A-Players Who:- Express our core values- Are hungry for knowledge- Want to learn new skills- Are respectful- Collaborate with others across the whole company- Share knowledge with coworkers- Educate and promote AI and ML concepts both internally and externally- Have a high work ethic and are self-motivated Our Perks:🌴Uncapped Vacation - For all full time, permanent employees. Seriously, take the time you need - when you need it.🚀Make an Impact - Witness the impact your work contribution has on the success of our company.👩🏿‍💻Working with PhD and Master Level Colleagues - Endless conversations around the latest in Machine Learning and Applied AI.🩺Competitive Benefits - For all full time, permanent employees. 🏢 Office as a Resource -  Hybrid work environment with state-of-the-art office spaces that ignite collaboration.⚡Big Slack Energy - IYKYK. Our Culture:You will be working in a high-paced environment focused on creating unique ML solutions to problems across multiple industries to generate impactful value. You will be working at a company with employees who have multiple years of industrial and academic experience in data science, software engineering, product development, and machine learning fields.  You will be able to experience a collaborative company culture, which means we believe in working hard, getting the job done, and enjoying the group social on Fridays. You’ll also get flexibility in where you work, what hours you work, how much vacation you take, and what you wear. We expect hard work but respect work/life balance. Core Values:At AltaML, we are driven by our core values. We believe that by embodying these values in everything we do, we will exceed our customer’s expectations while creating a positive and empowering work environment for our team members. We are dedicated to living our values and strive to make them the foundation for our success.  Gritty - We are entrepreneurial, determined, and resilient, pushing through challenges to achieve our goals. Agile - We make decisions based on “little bets” creating a safe space to take risks. We embrace an interactive process, allowing ideas to fail quickly or be scaled iteratively. Together, we continuously refine and improve our approach to reach the desired outcome.  Humble - We listen to data, embrace new ideas, admit limitations and take ownership to solve problems. We constantly push ourselves to improve and excel.   Happy - We ignite passion and purpose by fostering a dynamic work environment. We encourage tap dancing to work, common sense over rules, empowering team members to find joy in their contributions, and being your authentic self.

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We are looking for the bestAbout Us42dot은 소프트웨어와 AI로 모빌리티 문제를 해결하기 위해 노력하는 모빌리티 AI 기업입니다. 현대자동차그룹 글로벌 소프트웨어 센터로서, 42dot은 소프트웨어 정의 차량 개발을 선도하며 미래 모빌리티를 개척하고 있습니다.LLM Engineer (LLM Evaluation)는 대규모 언어 모델(LLM)의 성능을 신뢰성 있게 평가하고, 평가 결과를 기반으로 모델 품질을 지속적으로 개선할 수 있는 평가 체계와 플랫폼을 구축합니다.빠르게 변화하는 LLM 환경 속에서 benchmark dataset, evaluation protocol, automation pipeline을 설계하여 모델의 품질과 안정성을 지속적으로 향상시키고, 실서비스 수준의 검증 체계를 운영하는 데 기여합니다.또한 Kubernetes 기반 환경에서 Argo Workflows 및 MLflow를 활용하여 모델 평가–실험 관리–배포 검증까지 이어지는 end-to-end evaluation workflow를 구축하고, 반복 가능하고 재현성 있는 평가 환경을 고도화합니다.ResponsibilitiesLLM Evaluation & Benchmark 설계LLM 성능 평가를 위한 벤치마크 데이터셋 구축 및 평가 지표(Human/LLM-based) 설계공정한 모델 비교를 위한 평가 프로토콜 수립 및 재현성(Reproducibility) 확보Evaluation Automation 및 Workflow 연동Argo Workflows, MLflow 기반의 평가 자동화 환경 구축 및 ML 파이프라인 통합모델 배포 시 성능 저하(Regression) 자동 감지 및 알림 체계 설계Model Quality Validation 및 운영 고도화반복 가능한 평가 워크플로우를 통한 대규모 모델 품질 및 안정성 검증평가 결과를 바탕으로 한 지속적인 모델 품질 개선 프로세스 운영QualificationsLLM 학습 또는 평가 관련 분야 3년 이상 경력Deep Learning 또는 NLP 관련 연구 및 개발 경험LLM evaluation framework 사용 경험 (lm-eval, HELM, OpenAI Evals 등)Python 기반 서비스 개발 경험 (async/비동기 처리 포함)실험 관리 및 reproducibility에 대한 이해모델 평가 및 validation workflow 설계 경험동료와의 원활한 협업 능력Preferred QualificationsKubernetes 및 컨테이너 기반 환경 개발 경험대규모 데이터 처리 또는 pipeline 설계 경험GPU 기반 분산 inference 또는 대규모 모델 평가 경험Datadog, Prometheus 등을 활용한 모니터링 구축 경험MLflow, Argo Workflows 기반 ML workflow 운영 경험GPU 클러스터 기반 evaluation pipeline 설계 및 운영 경험LLM 품질 평가 자동화 및 운영 경험Interview Process서류전형 - 코딩테스트 - 화상면접 (1시간 내외) - 대면 혹은 화상면접 (3시간 내외) - 최종합격전형절차는 직무별로 다르게 운영될 수 있으며, 일정 및 상황에 따라 변동될 수 있습니다.전형일정 및 결과는 지원서에 등록하신 이메일로 개별 안내드립니다.Additional Information이력서 제출 시 주민등록번호, 가족관계, 혼인 여부, 연봉, 사진, 신체조건, 출신 지역 등 채용절차법상 요구 금지된 정보는 제외 부탁드립니다.모든 제출 파일은 30MB 이하의 PDF 양식으로 업로드를 부탁드립니다. (이력서 업로드 중 문제가 발생한다면 지원하시고자 하는 포지션의 URL과 함께 이력서를 recruit@42dot.ai으로 전송 부탁드립니다.)인터뷰 프로세스 종료 후 지원자의 동의하에 평판조회가 진행될 수 있습니다.국가보훈대상자 및 취업보호 대상자는 관계법령에 따라 우대합니다.장애인 고용 촉진 및 직업재활법에 따라 장애인 등록증 소지자를 우대합니다.42dot은 의뢰하지 않은 서치펌의 이력서를 받지 않으며, 요청하지 않은 이력서에 대해 수수료를 지불하지 않습니다.3개월의 수습기간이 적용될 수 있습니다.※ 지원 전 아래 내용을 꼭 확인해 주세요.42dot이 일하는 방식, 42dot Way 보러가기 →42dot만의 업무몰입 프로그램, Employee Engagement Program 보러가기 →

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Imagine having the power to stress-test an entire power grid against a hurricane or thunderstorm before the clouds even gather. That is the reality we are creating at Neara.We use advanced machine learning to create engineering-grade, physics enabled digital twins of electricity grids across four continents, this helps asset owners understand their biggest challenges and bring the most viable solutions to life across millions of kilometres of infrastructure.By simulating extreme weather and structural stress at a network-wide scale, we empower the world’s largest utilities to pinpoint risks, optimise investments and build a more resilient global energy future.Our team is a collection of brilliant minds who are fanatical about making a tangible difference in the real world, utilising AI and machine learning to accelerate everything from data classification to complex scenario analysis. We have built a special culture where innovation thrives because everyone owns the mission and we need smart, creative people to help us scale this impact to every corner of the globe.Data ScientistAs a Data Scientist, you will analyze a rich array of real-world data to inform our digital twin model of the electric grid, including topography, LIDAR, imagery, vegetation, structural loading, and electrical connectivity. Your work will drive product direction with high visibility, highlight grid expansion opportunities, identify aging and risky infrastructure, and help our customers understand where to build and invest. Working alongside ML Engineers and product-facing engineering teams, your ideas will ultimately take shape as new product features that expand what Neara is capable of doing, and as new infrastructure buildouts for the energy grid itself.What You Will Do:Model accurate digital twin electric networks from imperfect data using AI, deep learning, and classical ML algorithms.Surface meaningful analytics and metrics such as wildfire risk that help guide customer buildout of electrical infrastructure.Advise the company on what our data says and use that understanding to inform Neara’s strategy.Conduct experiments and A/B tests to improve our modeling of the grid.QA and improve our predictive models; identify data issues such as distribution drift, overfitting, or test set leakage.Craft scalable data pipelines to work with a variety of data sources, including LiDAR, aerial photography, photogrammetry and GIS.Mentor others in best practices for model training, data analytics, and building data-driven products.Who You Are:A data scientist, ML scientist, data engineer, or similar with 3-6 years of experience at technical, data-driven companies operating in complex environments. Geospatial data or power grid experience are a plus.You have a strong intuition for data with good communication skills and experience sharing your findings with customers and senior leaders.Demonstrated experience with AI and Machine Learning and a keen intuition for data modeling.Experience translating your models into both experiments and deployable software.Proficiency in data storage and ETL technologies, such as Parquet, Databricks, Snowflake, PostgreSQL, Spark, and DynamoDB.Comfort working in an AWS cloud environment.Excellent problem-solving skills as applied to new domains.Ability to work effectively asynchronously and cross-functionally on novel, cutting-edge problems.Ability to own problems and proactively approach challenges.Prior experience in the energy industry is a plus.What We Offer:Work with a sophisticated, multi-modal data stack, including LiDAR, satellite imagery, and physics-enabled digital twins, to solve high-stakes engineering problems that most data scientists only see in theory.Your models will directly prevent wildfires and mitigate disaster risks across millions of kilometers of infrastructure, moving beyond "digital metrics" to harden the real-world energy grid.You won't just build models; you’ll advise the company on data strategy and see your experiments evolve into core product features that dictate how the world’s largest utilities invest.We offer a highly competitive compensation package with a significant equity component, ensuring you are a true stakeholder in our mission and benefit directly from the company’s rapid global scale.

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Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans — including many of the top 20 and representing more than 270 million lives — Machinify brings together a fully configurable, content-rich, AI-powered platform along with best-in-class expertise. We're constantly reimagining what's possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs. The Role We're building production-grade agentic systems that audit medical claims end-to-end — reading raw medical records, reasoning over coding and clinical guidelines, and producing defensible findings that hold up to clinical and regulatory review. Reaching human-expert accuracy on noisy, long-context documents is one of the hardest unsolved problems in applied AI, and the field is moving weekly. We're hiring an L4 AI Engineer who can step into an ambiguous problem, design an agent system from scratch, and ship it. You won't be plugging into someone else's architecture — you'll be deciding what the architecture should be. What You'll Do - Design agent systems from first principles. Decide the loop, the tools, the context strategy, the evaluation harness. Choose between single-agent and multi-agent topologies, between LLM reasoning and deterministic post-passes, between retrieval and direct context loading — and defend the choice with data. - Engineer the context. The hardest part of building a good agent is what goes into the prompt and what comes out. You'll obsess over context windows, tool surfaces, structured outputs, citation grounding, and the prompt itself. - Drive evaluation rigor. Build evals before you build the agent. Diagnose where it fails, fix the root cause, and prove the fix moved the metric. - Use AI tooling like a power user. A meaningful fraction of your day will be spent driving Claude Code, Codex, and similar tools to plan, scaffold, refactor, and debug your own work. We expect you to be faster with these tools than most engineers are without them. - Become a domain expert. Healthcare claims, coding guidelines, and the medical record itself are unavoidable parts of the job. Strong engineers who lean into the domain become outsized contributors here. What We're Looking For Required - 2–4 years of applied ML / AI engineering experience with a Bachelor's in CS, Math, Engineering or equivalent — or a Master's in a similar program with no prior industry experience required. Either way, at least one production-quality system (industry, research, or substantial open-source) you owned end-to-end. - Strong Python engineering. Clean abstractions, type discipline, async, tested code. - Deep, hands-on understanding of agent loops — how a model decides to call a tool, how a tool result re-enters context, how loops terminate, where they fail. - Hands-on experience with at least one major agent SDK — OpenAI Agents SDK, Anthropic SDK / claude-agent-sdk, LangGraph, or equivalent — and an opinion on the tradeoffs. - Working knowledge of how modern coding agents are built and how they engineer context — what goes in the system prompt, how files are read and edited, how long-running tasks are planned and tracked, where they break. - Fluency with Claude Code / Codex as a power user. You should be able to brainstorm, plan, and execute non-trivial engineering tasks with these tools — including reading their source when needed to understand or extend behavior. - Solid command of VS Code and git — branches, rebases, worktrees, conflict resolution, PR workflows. Not optional. - A bias toward measurement: you don't ship without an eval, and you don't believe a number you can't reproduce. Strongly preferred - Experience designing structured outputs (Pydantic / JSON Schema) and tool interfaces that LLMs reliably call correctly. - Familiarity with reasoning models (o-series, Claude extended thinking, Gemini thinking) and a sense of when they earn their cost. - Prior work on long-context, citation-grounded systems where the model must point to evidence, not just answer. - Healthcare, legal, finance, or any other domain where "mostly right" is unacceptable. Nice to have - Document understanding (OCR, layout-aware models, table extraction). - Vision-language models, multimodal retrieval. - Production experience with caching, observability, and cost control on LLM workloads. What We Offer  Work from anywhere in the US! Machinify is digital-first. Top Medical/Dental/Vision offerings FSA/HSA Tuition reimbursement Competitive salary, 401(k) with company match Unlimited PTO Additional health and wellness benefits and perks Flexible and trusting environment where you’ll feel empowered to do your best work  The salary for this position is based on an array of factors unique to each candidate: Such as years and depth of experience, set skills, certifications, etc. We are hiring for different levels and the base salary can range from $130k-$200k+ based on your assessed level. Compensation also includes meaningful equity, healthcare, unlimited PTO, and more.Equal Employment Opportunity at Machinify   We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace. Machinify is an employment at will employer. We participate in E-Verify as required by applicable law. In accordance with applicable state laws, we do not inquire about salary history during the recruitment process. If you require a reasonable accommodation to complete any part of the application or recruitment process, please let our recruiters know. See our Candidate Privacy Notice at: https://www.machinify.com/candidate-privacy-notice/

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Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans — including many of the top 20 and representing more than 270 million lives — Machinify brings together a fully configurable, content-rich, AI-powered platform along with best-in-class expertise. We're constantly reimagining what's possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs. The Role We're building production-grade agentic systems that audit medical claims end-to-end — reading raw medical records, reasoning over coding and clinical guidelines, and producing defensible findings that hold up to clinical and regulatory review. Reaching human-expert accuracy on noisy, long-context documents is one of the hardest unsolved problems in applied AI, and the field is moving weekly. We're hiring an L4 AI Engineer who can step into an ambiguous problem, design an agent system from scratch, and ship it. You won't be plugging into someone else's architecture — you'll be deciding what the architecture should be. What You'll Do - Design agent systems from first principles. Decide the loop, the tools, the context strategy, the evaluation harness. Choose between single-agent and multi-agent topologies, between LLM reasoning and deterministic post-passes, between retrieval and direct context loading — and defend the choice with data. - Engineer the context. The hardest part of building a good agent is what goes into the prompt and what comes out. You'll obsess over context windows, tool surfaces, structured outputs, citation grounding, and the prompt itself. - Drive evaluation rigor. Build evals before you build the agent. Diagnose where it fails, fix the root cause, and prove the fix moved the metric. - Use AI tooling like a power user. A meaningful fraction of your day will be spent driving Claude Code, Codex, and similar tools to plan, scaffold, refactor, and debug your own work. We expect you to be faster with these tools than most engineers are without them. - Become a domain expert. Healthcare claims, coding guidelines, and the medical record itself are unavoidable parts of the job. Strong engineers who lean into the domain become outsized contributors here. What We're Looking For Required - 2–4 years of applied ML / AI engineering experience with a Bachelor's in CS, Math, Engineering or equivalent — or a Master's in a similar program with no prior industry experience required. Either way, at least one production-quality system (industry, research, or substantial open-source) you owned end-to-end. - Strong Python engineering. Clean abstractions, type discipline, async, tested code. - Deep, hands-on understanding of agent loops — how a model decides to call a tool, how a tool result re-enters context, how loops terminate, where they fail. - Hands-on experience with at least one major agent SDK — OpenAI Agents SDK, Anthropic SDK / claude-agent-sdk, LangGraph, or equivalent — and an opinion on the tradeoffs. - Working knowledge of how modern coding agents are built and how they engineer context — what goes in the system prompt, how files are read and edited, how long-running tasks are planned and tracked, where they break. - Fluency with Claude Code / Codex as a power user. You should be able to brainstorm, plan, and execute non-trivial engineering tasks with these tools — including reading their source when needed to understand or extend behavior. - Solid command of VS Code and git — branches, rebases, worktrees, conflict resolution, PR workflows. Not optional. - A bias toward measurement: you don't ship without an eval, and you don't believe a number you can't reproduce. Strongly preferred - Experience designing structured outputs (Pydantic / JSON Schema) and tool interfaces that LLMs reliably call correctly. - Familiarity with reasoning models (o-series, Claude extended thinking, Gemini thinking) and a sense of when they earn their cost. - Prior work on long-context, citation-grounded systems where the model must point to evidence, not just answer. - Healthcare, legal, finance, or any other domain where "mostly right" is unacceptable. Nice to have - Document understanding (OCR, layout-aware models, table extraction). - Vision-language models, multimodal retrieval. - Production experience with caching, observability, and cost control on LLM workloads. What We Offer  Work from anywhere in the US! Machinify is digital-first. Top Medical/Dental/Vision offerings FSA/HSA Tuition reimbursement Competitive salary, 401(k) with company match Unlimited PTO Additional health and wellness benefits and perks Flexible and trusting environment where you’ll feel empowered to do your best work  The salary for this position is based on an array of factors unique to each candidate: Such as years and depth of experience, set skills, certifications, etc. We are hiring for different levels and the base salary can range from $130k-$200k+ based on your assessed level. Compensation also includes meaningful equity, healthcare, unlimited PTO, and more.Equal Employment Opportunity at Machinify   We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace. Machinify is an employment at will employer. We participate in E-Verify as required by applicable law. In accordance with applicable state laws, we do not inquire about salary history during the recruitment process. If you require a reasonable accommodation to complete any part of the application or recruitment process, please let our recruiters know. See our Candidate Privacy Notice at: https://www.machinify.com/candidate-privacy-notice/

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Who are we?Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. We like to work hard and move fast to do what’s best for our customers.Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products.Join us on our mission and shape the future!As the Head of Solutions Architecture at Cohere, you will be the strategic leader responsible for leading a global team of Solutions Architects. In this role you will drive sales and revenue growth by designing and delivering cutting-edge AI solutions that meet the needs of enterprise customers worldwide. This role is pivotal in shaping Cohere’s technical narrative, ensuring our solutions align with customer requirements, and directly contributing to the company’s bottom line. In this leadership role you will be both a visionary architect and a results-driven sales leader, bridging the gap between technical innovation and business outcomes.In this role, you will:Drive Revenue Growth: Lead the development and execution of technical sales strategies that directly contribute to revenue targets and market expansion.Scale the Team: Build, mentor, and scale a high-performing global solutions architecture team, ensuring they are equipped to win deals and deliver value to customers.Own the Sales Pipeline: Partner with the sales team to identify, pursue, and close high-value opportunities, serving as the technical expert and trusted advisor to customers.Design Winning Solutions: Architect scalable, secure, and customizable AI solutions that address complex enterprise challenges and differentiate Cohere in the market.Shape Product Development: Collaborate closely with the product team to ensure Cohere’s platform evolves to meet customer needs and drives competitive advantage.Establish Best Practices: Define and implement industry-leading best practices for agentic AI, model customization, and enterprise deployment, ensuring Cohere remains at the forefront of innovation.Optimize Sales Processes: Streamline technical sales processes, from proposal development to customer onboarding, to maximize efficiency and win rates.Key Responsibilities:Architect and deliver end-to-end AI solutions. Design scalable, secure, and customizable NLP and generative AI solutions tailored to complex enterprise workflows.Lead the team and technical engagements with C-suite executives and stakeholders, including workshops, deep dives, and solution presentations.Oversee the deployment and integration of LLMs and custom solutions into production environments, ensuring high performance, security, and scalability.Mentor and guide the solutions architecture team, fostering technical excellence and sales leadership skills.Collaborate with cross-functional teams to ensure seamless alignment between customer needs, product development, and technical execution.Identify and cultivate technical champions within customer organizations to drive adoption and gather actionable feedback.Partner with the sales leadership team to develop and execute global sales strategies, including technical enablement and customer onboarding.Track and report on key sales metrics, including pipeline health, win rates, and revenue contribution.What We’re Looking For:Experience: 10+ years of experience in AI/ML solution architecture, with a proven track record of leading large-scale enterprise projects and teams.Sales Engineering Leadership: 10+ years of experience leading sales engineering teams, with a focus on driving revenue growth and managing global sales efforts.Technical Expertise: Deep knowledge of agentic AI, model customization, and enterprise deployment.Revenue Focus: Demonstrated ability to translate technical solutions into business value and directly contribute to revenue targets.Leadership Skills: Proven ability to build, mentor, and scale high-performing technical teams in a fast-paced, global environment.Strategic Thinking: Strong ability to align technical solutions with business objectives and drive innovation.Communication Skills: Exceptional ability to articulate complex technical concepts to both technical and executive audiences.Enterprise Focus: Experience designing and deploying AI solutions for large enterprises, with a focus on scalability, security, and customization.Global Perspective: Proven experience managing cross-regional teams and adapting strategies to diverse markets.Preferred Qualifications:Background in building and managing scalable AI/ML ecosystems, including multi-cloud deployment strategies.Familiarity with security standards for deploying agent-based AI solutions, including data privacy, model safety, and access controls.Experience working in a startup-like context with a track record of driving rapid growth and innovation.Knowledge of agent orchestration frameworks like Cohere North and experience deploying custom agents to production.Experience with CRM and sales enablement tools (e.g., Salesforce, HubSpot) and technical sales methodologies.Track record of exceeding sales targets and delivering measurable revenue growth.If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.Full-Time Employees at Cohere enjoy these Perks:🤝 An open and inclusive culture and work environment 🧑‍💻 Work closely with a team on the cutting edge of AI research 🍽 Weekly lunch stipend, in-office lunches & snacks🦷 Full health and dental benefits, including a separate budget to take care of your mental health 🐣 100% Parental Leave top-up for up to 6 months🎨 Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement🏙 Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend✈️ 6 weeks of vacation (30 working days!)

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About AbridgeAbridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients.Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems.We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh. The RoleOur generative AI-powered products are transforming the practice of medicine—and the inference systems that power them need to be fast, reliable, and world-class. We’re looking for an Engineering Manager to lead and grow our Model Inference team.The Inference team owns the end-to-end technical direction of how our models are served: from architecting low-latency, high-throughput infrastructure to pushing the frontier of LLM serving techniques. You’ll lead a high-performing team of AI inference engineers, partner closely with ML Research and the broader AI Platform, and ensure the systems underpinning every clinician interaction are operating at peak efficiency and reliability.What You’ll DoLead and grow a high-performing team of AI inference engineers focused on building and scaling infrastructure for Abridge’s products and APIsOwn the technical direction of our inference systems—making key decisions around batching, throughput, latency, and GPU utilizationArchitect and scale inference infrastructure for reliability, efficiency, and observability; lead incident responseBenchmark and eliminate bottlenecks throughout the inference stackPartner with ML Research teams on model optimization, quantization, and deploymentDevelop APIs for AI inference used by both internal teams and external customersRecruit, mentor, and develop engineering talent; establish team processes, engineering standards, and operational excellenceWork closely with the GenAI Platform, Data, and Product teams to plan and execute projects that directly impact clinicians and patientsWhat You’ll Bring5+ years of engineering experience with 1+ years in a technical leadership or management roleDeep, hands-on experience with ML systems and inference frameworks (e.g., PyTorch, TensorRT, vLLM, TensorFlow)Strong understanding of LLM architecture (eg. Multi-Head Attention, Multi/Grouped-Query Attention, and common transformer components)Experience with inference optimizations (eg. batching, quantization, kernel fusion, FlashAttention)Familiarity with GPU characteristics, roofline models, and performance analysisExperience deploying reliable, distributed, real-time systems at scaleExperience with parallelism strategies: tensor parallelism, pipeline parallelism, expert parallelismSkilled at hiring and mentorship, with a demonstrated track record of helping engineers grow their skills and careersStrong technical communication and cross-functional collaboration skillsComfortable giving constructive feedback on technical designs and code reviewsHas thrived in a fast-growing startup and knows how to operate with urgency and focusAdded BonusBackground in training infrastructure and RL workloadsSkilled in building secure, compliant systems on major cloud platforms (GCP preferred, AWS experience welcome)Experience with Kubernetes and container orchestration at scalePublished work or contributions to inference optimization researchWhy Work at Abridge?At Abridge, we’re transforming healthcare delivery experiences with generative AI, enabling clinicians and patients to connect in deeper, more meaningful ways. Our mission is clear: to power deeper understanding in healthcare. We’re driving real, lasting change, with millions of medical conversations processed each month.Joining Abridge means stepping into a fast-paced, high-growth startup where your contributions truly make a difference. Our culture requires extreme ownership—every employee has the ability to (and is expected to) make an impact on our customers and our business.Beyond individual impact, you will have the opportunity to work alongside a team of curious, high-achieving people in a supportive environment where success is shared, growth is constant, and feedback fuels progress. At Abridge, it’s not just what we do—it’s how we do it. Every decision is rooted in empathy, always prioritizing the needs of clinicians and patients.We’re committed to supporting your growth, both professionally and personally. Whether it's flexible work hours, an inclusive culture, or ongoing learning opportunities, we are here to help you thrive and do the best work of your life.If you are ready to make a meaningful impact alongside passionate people who care deeply about what they do, Abridge is the place for you. How we take care of Abridgers:Generous Time Off: 14 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employeesComprehensive Health Plans: Medical, Dental, and Vision coverage for all full-time employees and their families.Generous HSA Contribution: If you choose a High Deductible Health Plan, Abridge makes monthly contributions to your HSA.Paid Parental Leave: Generous paid parental leave for all full-time employees.Family Forming Benefits: Resources and financial support to help you build your family.401(k) Matching: Contribution matching to help invest in your future.Personal Device Allowance: Tax free funds for personal device usage.Pre-tax Benefits: Access to Flexible Spending Accounts (FSA) and Commuter Benefits.Lifestyle Wallet: Monthly contributions for fitness, professional development, coworking, and more.Mental Health Support: Dedicated access to therapy and coaching to help you reach your goals.Sabbatical Leave: Paid Sabbatical Leave after 5 years of employment.Compensation and Equity: Competitive compensation and equity grants for full time employees.... and much more!Equal Opportunity EmployerAbridge is an equal opportunity employer and considers all qualified applicants equally without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability.Staying safe - Protect yourself from recruitment fraudWe are aware of individuals and entities fraudulently representing themselves as Abridge recruiters and/or hiring managers. Abridge will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Abridge recruiting team will come from an @abridge.com email address. You can learn more about how to protect yourself from these types of fraud by referring to this article. Please exercise caution and cease communications if something feels suspicious about your interactions.