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About LuminaiNearly every organization in the world relies on complex manual work to carry out critical internal processes. These are processes that keep the world going — enrolling patients in a hospital, underwriting loans inside a bank, or processing new transactions for an airline. Yet most companies don’t have enough resources to properly automate these tasks and are stuck in manual, decades old way of doing things. At Luminai, we develop technology to automate long-form organization wide workflows of any complexity easily and safely using AI. Luminai serves some of the world’s most critical organizations in sectors like Healthcare, Finance, and Telecommunication to delegate mission-critical workflows that previously required hands-on human involvement, over to autonomous AI systems. Our approach combines frontier AI development, with a purpose built workflow execution engine to achieve this goal. We've raised significant amounts of capital (including some un-announced) from many of the best Silicon Valley VCs: General Catalyst, YCombinator, and investors including Kevin Weil (Chief Product Officer at OpenAI), Arash Ferdowsi (co-founder of Dropbox), Katie Stanton (former VP Global Media, Twitter) and CEOs of companies including Flexport, Notion, Front, Ramp and Twitch.About the roleAs a Software Engineer working on AI systems, you will play a foundational role in research, experimentation and rapid improvement of AI systems towards building a capable, reliable AI automation platform. The platform is used by organizations worldwide to deploy and scale executable AI automations in mission critical production environments. You are expected to have a strong proficiency in fundamentals of software engineering, a willingness to pick new concepts as needed and an ability to drive technical projects in ambitious environments. You might work onDesign experiments and test ideas to optimize key internal AI benchmarksDesign and improve evaluation frameworks to accelerate the speed and direction of experimentationTrain, fine-tune, and optimize machine learning models. Perform rigorous evaluation and testing to ensure model accuracy, generalization, and performance.Collaborate and contribute on the core product development to deliver higher platform capabilitiesSetup up observability and monitoring systems to safety check model behaviour in critical settings Things we're looking forProven track record of shipping high-quality code in challenging projectsBachelor's or Master's degree in Computer Science, Machine Learning, or a related fieldSolid fundamentals in algorithms, data structures, system designAttention to detail and a first-principals thinking towards real world deployment of intelligent systems.Bonus PointsProficiency in C++Previous experience working with distributed computing systems in production

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Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA. Figure’s vision is to deploy autonomous humanoids at a global scale. Our Helix team is looking for an experienced Training Infrastructure Engineer, to take our infrastructure to the next level. This role is focused on managing the training cluster, implementing distributed training algorithms, data loaders, and developer tools for AI researchers. The ideal candidate has experience building tools and infrastructure for a large-scale deep learning system. Responsibilities Design, deploy, and maintain Figure's training clusters Architect and maintain scalable deep learning frameworks for training on massive robot datasets Work together with AI researchers to implement training of new model architectures at a large scale Implement distributed training and parallelization strategies to reduce model development cycles Implement tooling for data processing, model experimentation, and continuous integration Requirements Strong software engineering fundamentals Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field Experience with Python and PyTorch Experience managing HPC clusters for deep neural network training Minimum of 4 years of professional, full-time experience building reliable backend systems Bonus Qualifications Experience managing cloud infrastructure (AWS, Azure, GCP) Experience with job scheduling / orchestration tools (SLURM, Kubernetes, LSF, etc.) Experience with configuration management tools (Ansible, Terraform, Puppet, Chef, etc.) The US base salary range for this full-time position is between $150,000 - $350,000 annually. The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.

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About CartesiaOur mission is to build the next generation of AI: ubiquitous, interactive intelligence that runs wherever you are. Today, not even the best models can continuously process and reason over a year-long stream of audio, video and text—1B text tokens, 10B audio tokens and 1T video tokens—let alone do this on-device.We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences.We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI.About the RoleWe’re hiring a Forward Deployed Engineer to advance our mission of building real-time multimodal intelligence by delivering agentic voice AI solutions directly into production for our customers.Your Impact: Be the driving force behind customer deployments, taking AI solutions from early concept and pilot to production launch with enterprise-grade reliabilityTranslate cutting-edge AI capabilities into practical, high-performance systems tailored to real-world customer needsDesign and implement agentic voice AI solutions that integrate seamlessly into customer workflows and infrastructurePrototype, iterate, and deploy AI-driven systems in close collaboration with enterprise customersWork closely with our customers to define success criteria and ensure they achieve meaningful outcomes on Cartesia’s platformYou’ll have significant autonomy to shape customer solutions and directly impact how cutting-edge AI is deployed at scale across global organizations What You BringTechnical leadership with the ability to execute and deliver zero-to-one solutions in ambiguous, customer-driven environmentsYou have an eye for identifying customer problems and opportunities and can translate them into effective AI-powered solutionsStrong engineering skills enable you to rapidly prototype solutions end to end and evolve them into scalable, production-ready systemsYou’re comfortable diving into new technologies and can quickly adapt your skills to our tech stack (Python on the backend, Go and TypeScript preferred)You communicate complex technical concepts clearly and effectively, and you’re comfortable working directly with customersYou’re good at collaborating cross-functionally and translating customer feedback into actionable product and platform improvementsOur Culture🏢 We’re an in-person team based out of San Francisco. We love being in the office, hanging out together, and learning from each other every day.🚢 We ship fast. All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don’t sacrifice quality or design along the way.🤝 We support each other. We have an open & inclusive culture that’s focused on giving everyone the resources they need to succeed.

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Our MissionReflection’s mission is to build open superintelligence and make it accessible to all.We’re developing open weight models for individuals, agents, enterprises, and even nation states. Our team of AI researchers and company builders come from DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic and beyond.About the RoleData is playing an increasingly crucial role at the frontier of AI innovation. Many of the most meaningful advances in recent years have come not from new architectures, but from better data.As a member of the Data Team, your mission is to ensure that the data used to train our models meets a high bar for quality, reliability, and downstream impact. You will directly shape how our models perform on critical capabilities.Working with world-class researchers on our pre-training teams, you’ll help turn fuzzy notions of “good data” into concrete, measurable standards that scale across large data campaigns. We’re looking for engineers who combine strong engineering fundamentals with a deep curiosity about data quality and its impact on model performance.Working closely with our pre-training teams you will:Own upstream data quality for LLM pre-training; as a specialist or generalist across languages and modalitiesPartner closely with research and pre-training teams to translate requirements into measurable quality signals, and provide actionable feedback to external data vendorsIn addition to human-in-the-loop processes, you will design, validate, and scale automated QA methods to reliably measure data quality across large campaignsBuild reusable QA pipelines that reliably deliver high-quality data to pre-training teams for model trainingMonitor and report on data quality over time, driving continuous iteration on quality standards, processes, and acceptance criteriaAbout YouStrong engineering fundamentals with experience building data pipelines, QA systems, or evaluation workflows for pre-training dataDetail-oriented with an analytical mindset, able to identify failure modes, inconsistencies, and subtle issues that affect data qualitySolid understanding of how data quality impacts pre-training, with the ability to translate quality concerns into concrete signals, decisions, and feedbackExperience designing and validating automated quality checks, including rule-based systems, statistical methods, or model-assisted approaches such as LLM-as-a-JudgeComfortable working autonomously, owning problems end-to-end, and collaborating effectively with researchers, engineers, and operations partnersSkills and QualificationsProficiency in Python and building ML / LLM workflows. Must be comfortable debugging and writing scalable codeExperience working with large datasets and automated evaluation or quality-checking systemsFamiliarity with how LLMs work and can describe how models are trained and evaluatedExcellent communication skills with the ability to clearly articulate complex technical concepts across teamsWhat We Offer:We believe that to build superintelligence that is truly open, you need to start at the foundation. Joining Reflection means building from the ground up as part of a small talent-dense team. You will help define our future as a company, and help define the frontier of open foundational models.We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.Top-tier compensation: Salary and equity structured to recognize and retain the best talent globally.Health & wellness: Comprehensive medical, dental, vision, life, and disability insurance.Life & family: Fully paid parental leave for all new parents, including adoptive and surrogate journeys. Financial support for family planning.Benefits & balance: paid time off when you need it, relocation support, and more perks that optimize your time. Opportunities to connect with teammates: lunch and dinner are provided daily. We have regular off-sites and team celebrations.

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Our MissionReflection’s mission is to build open superintelligence and make it accessible to all.We’re developing open weight models for individuals, agents, enterprises, and even nation states. Our team of AI researchers and company builders come from DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic and beyond.About the RoleData is playing an increasingly crucial role at the frontier of AI innovation. Many of the most meaningful advances in recent years have come not from new architectures, but from better data.As a member of the Data Team, your mission is to ensure that the data used to train and evaluate our models meets a high bar for quality, reliability, and downstream impact. You will directly shape how our models perform on critical capabilities — agentic tool use, long-horizon reasoning and robust safety alignment.Working with world-class researchers on our post-training teams, you’ll help turn fuzzy notions of “good data” into concrete, measurable standards that scale across large data campaigns. We’re looking for engineers who combine strong engineering fundamentals with a deep curiosity about data quality and its impact on model behaviorWorking closely with our post-training teams you will:Own upstream data quality for LLM post-training and evaluation by analyzing expert-developed datasets and operationalizing quality standards for reasoning, alignment, and agentic use casesPartner closely with research and post-training teams to translate requirements into measurable quality signals, and provide actionable feedback to external data vendorsDesign, validate, and scale automated QA methods, including LLM-as-a-Judge frameworks, to reliably measure data quality across large campaignsBuild reusable QA pipelines that reliably deliver high-quality data to post-training teams for model training and evaluationMonitor and report on data quality over time, driving continuous iteration on quality standards, processes, and acceptance criteriaAbout YouStrong engineering fundamentals with experience building data pipelines, QA systems, or evaluation workflows for post-training data and agentic environmentsDetail-oriented with an analytical mindset, able to identify failure modes, inconsistencies, and subtle issues that affect data qualitySolid understanding of how data quality impacts training (SFT and RL) and evaluation, with the ability to translate quality concerns into concrete signals, decisions, and feedbackExperience designing and validating automated quality checks, including rule-based systems, statistical methods, or model-assisted approaches such as LLM-as-a-JudgeComfortable working autonomously, owning problems end-to-end, and collaborating effectively with researchers, engineers, and operations partnersSkills and QualificationsProficiency in Python and building ML / LLM workflows. Must be comfortable debugging and writing scalable codeExperience working with large datasets and automated evaluation or quality-checking systemsFamiliarity with how LLMs work and can describe how models are trained and evaluatedExcellent communication skills with the ability to clearly articulate complex technical concepts across teamsWhat We Offer:We believe that to build superintelligence that is truly open, you need to start at the foundation. Joining Reflection means building from the ground up as part of a small talent-dense team. You will help define our future as a company, and help define the frontier of open foundational models.We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.Top-tier compensation: Salary and equity structured to recognize and retain the best talent globally.Health & wellness: Comprehensive medical, dental, vision, life, and disability insurance.Life & family: Fully paid parental leave for all new parents, including adoptive and surrogate journeys. Financial support for family planning.Benefits & balance: paid time off when you need it, relocation support, and more perks that optimize your time. Opportunities to connect with teammates: lunch and dinner are provided daily. We have regular off-sites and team celebrations.

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About SolidroadBuilding something great is addictive. It’s like discovering your favorite TV show mid-season, except you're helping write the next episode. It’s like unboxing a new gadget, except you’re the one designing it. You shape how it feels, how it works, and how people react when they use it.At Solidroad we're solving a big, messy, exciting problem: how do you scale customer support without losing what makes humans amazing? Warmth, empathy, intuition. We’re using AI, generative tech, and real-time simulations to make this happen. It's ambitious, difficult, and genuinely new.About the roleWe're looking for a senior engineer who sees this role as more than just a job. It should feel like signing up for a job and a hobby. Or a job and a triathlon. Or a job and an advanced language course. You’ll shape our technical decisions, own major parts of our product, mentor your teammates, and push yourself in ways you might not expect. It’ll stretch your skills and fill your life in a deeply rewarding way. You’ll write impactful code (full-stack, frontend, backend, AI integrations; it's all on the table), and make critical decisions about architecture, tooling, and product direction. There will be challenging moments, but also regular moments of genuine "wow, we built that" pride.What you'll actually do day-to-dayLead Development of Advanced Prompt Engineering and Retrieval Pipelines Architect and build scalable solutions handling thousands of customer conversations daily across integrations like Zendesk, Intercom, and Salesforce. You’ll leverage cutting-edge generation pipelines to deliver rapid insights, accurate analysis, and meaningful customer interaction scoring.Architect Real-Time Multimodal Simulations Own the development of realistic, interactive training experiences across voice, video, and chat using platforms like OpenAI, ElevenLabs, and Vapi. Your simulations will dynamically adapt to user input, creating genuinely immersive learning environments.Drive High-Performance User Experiences Set the technical standard for intuitive, lightning-fast interfaces built with Next.js. You'll ensure our UIs handle complex AI interactions seamlessly, even under demanding workloads, and mentor others to achieve the same standard.Our Team & CultureWe’re a focused group working full-time in our San Francisco and Dublin offices. We’re there five days a week because building something ambitious means collaborating closely, solving problems together, sharing ideas spontaneously, and occasionally challenging each other to padel matches, cold plunges, or karaoke evenings. We're intentional about creating a culture that's mature, supportive, and fun. You'll be joining experienced founders who've done this before, alongside a tight-knit team genuinely excited about building something impactful.We hope youHave at least 4 years of engineering experience, ideally at a startup (Series A to C), where you've helped ship complex, customer-facing products.Bring strong technical leadership, including experience mentoring other engineers, leading technical decisions, or architecting significant product areas.Have deep expertise in modern web technologies, product architecture, or AI integrations—bonus points if you’ve worked on notable or challenging projects we can explore together.The good, the bad, and the honest truthThe highs here are incredibly high, direct collaboration with founders who’ve done this before, significant ownership, and a product you'll feel deeply proud of. The lows are that you'll need to move fast, navigate ambiguity, and sometimes tackle tough problems solo. But the reality is that startups are a bit like a sport or an instrument: you invest in it not just because you have to, but because you genuinely want to be better tomorrow than you are today.If that sounds exciting, don’t second-guess yourself. Get in touch. Send us something you've built that you're proud of (GitHub, personal site, anything goes) and a short note about who you are and why this feels right to you. We value enthusiasm, ambition, and curiosity just as much as your technical experience.

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About SolidroadBuilding something great is addictive. It’s like discovering your favorite TV show mid-season, except you're helping write the next episode. It’s like unboxing a new gadget, except you’re the one designing it. You shape how it feels, how it works, and how people react when they use it.At Solidroad we're solving a big, messy, exciting problem: how do you scale customer support without losing what makes humans amazing? Warmth, empathy, intuition. We’re using AI, generative tech, and real-time simulations to make this happen. It's ambitious, difficult, and genuinely new.About the roleWe're looking for a senior engineer who sees this role as more than just a job. It should feel like signing up for a job and a hobby. Or a job and a triathlon. Or a job and an advanced language course. You’ll shape our technical decisions, own major parts of our product, mentor your teammates, and push yourself in ways you might not expect. It’ll stretch your skills and fill your life in a deeply rewarding way. You’ll write impactful code (full-stack, frontend, backend, AI integrations; it's all on the table), and make critical decisions about architecture, tooling, and product direction. There will be challenging moments, but also regular moments of genuine "wow, we built that" pride.What you'll actually do day-to-dayLead Development of Advanced Prompt Engineering and Retrieval Pipelines Architect and build scalable solutions handling thousands of customer conversations daily across integrations like Zendesk, Intercom, and Salesforce. You’ll leverage cutting-edge generation pipelines to deliver rapid insights, accurate analysis, and meaningful customer interaction scoring.Architect Real-Time Multimodal Simulations Own the development of realistic, interactive training experiences across voice, video, and chat using platforms like OpenAI, ElevenLabs, and Vapi. Your simulations will dynamically adapt to user input, creating genuinely immersive learning environments.Drive High-Performance User Experiences Set the technical standard for intuitive, lightning-fast interfaces built with Next.js. You'll ensure our UIs handle complex AI interactions seamlessly, even under demanding workloads, and mentor others to achieve the same standard.Our Team & CultureWe’re a focused group working full-time in our San Francisco and Dublin offices. We’re there five days a week because building something ambitious means collaborating closely, solving problems together, sharing ideas spontaneously, and occasionally challenging each other to padel matches, cold plunges, or karaoke evenings. We're intentional about creating a culture that's mature, supportive, and fun. You'll be joining experienced founders who've done this before, alongside a tight-knit team genuinely excited about building something impactful.We hope youHave at least 4 years of engineering experience, ideally at a startup (Series A to C), where you've helped ship complex, customer-facing products.Bring strong technical leadership, including experience mentoring other engineers, leading technical decisions, or architecting significant product areas.Have deep expertise in modern web technologies, product architecture, or AI integrations—bonus points if you’ve worked on notable or challenging projects we can explore together.The good, the bad, and the honest truthThe highs here are incredibly high, direct collaboration with founders who’ve done this before, significant ownership, and a product you'll feel deeply proud of. The lows are that you'll need to move fast, navigate ambiguity, and sometimes tackle tough problems solo. But the reality is that startups are a bit like a sport or an instrument: you invest in it not just because you have to, but because you genuinely want to be better tomorrow than you are today.If that sounds exciting, don’t second-guess yourself. Get in touch. Send us something you've built that you're proud of (GitHub, personal site, anything goes) and a short note about who you are and why this feels right to you. We value enthusiasm, ambition, and curiosity just as much as your technical experience.

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About the role We’re hiring an AI Architect to sit at the intersection of frontier AI research, product, and go-to-market. You’ll partner closely with ML teams in high-stakes meetings, scope and pitch solutions to top AI labs, and translate research needs (post-training, evals, alignment) into clear product roadmaps and measurable outcomes. You’ll drive end-to-end delivery—partnering with AI research teams and core customers to scope, pilot, and iterate on frontier model improvements—while coordinating with engineering, ops, and finance to translate cutting-edge research into deployable, high-impact solutions. What you’ll do Translate research → product: work with client side researchers on post-training, evals, safety/alignment and build the primitives, data, and tooling they need. Partner deeply with core customers and frontier labs: work hands-on with leading AI teams and frontier research labs to tackle hard, open-ended technical problems related to frontier model improvement, performance, and deployment. Shape and propose model improvement work: translate customer and research objectives into clear, technically rigorous proposals—scoping post-training, evaluation, and safety work into well-defined statements of work and execution plans. Translate research into production impact: collaborate with customer-side researchers on post-training, evaluations, and alignment, and help design the data, primitives, and tooling required to improve frontier models in practice. Own the end-to-end lifecycle: lead discovery, write crisp PRDs and technical specs, prioritize trade-offs, run experiments, ship initial solutions, and scale successful pilots into durable, repeatable offerings. Lead complex, high-stakes engagements: independently run technical working sessions with senior customer stakeholders; define success metrics; surface risks early; and drive programs to measurable outcomes. Partner across Scale: collaborate closely with research (agents, browser/SWE agents), platform, operations, security, and finance to deliver reliable, production-grade results for demanding customers. Build evaluation rigor at the frontier: design and stand up robust evaluation frameworks (e.g., RLVR, benchmarks), close the loop with data quality and feedback, and share learnings that elevate technical execution across accounts. You have Deep technical background in applied AI/ML: 5–10+ years in research, engineering, solutions engineering, or technical product roles working on LLMs or multimodal systems, ideally in high-stakes, customer-facing environments. Hands-on experience with model improvement workflows: demonstrated experience with post-training techniques, evaluation design, benchmarking, and model quality iteration. Ability to work on hard, ambiguous technical problems: proven track record of partnering directly with advanced customers or research teams to scope, reason through, and execute on deep technical challenges involving frontier models. Strong technical fluency: you can read papers, interrogate metrics, write or review complex Python/SQL for analysis, and reason about model-data trade-offs. Executive presence with world-class researchers and enterprise leaders; excellent writing and storytelling. Bias to action: you ship, learn, and iterate. How you’ll work Customer-obsessed: start from real research needs; prototype quickly; validate with data. Cross-functional by default: align research, engineering, ops, and GTM on a single plan; communicate clearly up and down. Field-forward: expect regular customer time and research leads; light travel as needed. What success looks like Clear wins with top labs: pilots that convert to scaled programs with strong eval signals. Reusable alignment & eval building blocks that shorten time-to-value across accounts. Crisp internal docs (PRDs, experiment readouts, exec updates) that drive decisions quickly. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:$190,000—$230,000 USDPLEASE 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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Who we are:TrustLab deploys cutting edge solutions to evaluate AI agents, models and apps for enterprise customers. With a 5 year track record working with large and small clients including social media companies and digital market places, and guided by founders who previously worked in senior leadership positions at Google, YouTube, TikTok, and Reddit, we are creating industry leading LLM based solutions for agentic system evaluation and labeling. Our approach includes human-in-the-loop and LLM-as-a-judge technologies, with a focus on rapid innovation and production level scaling. You’ll join a small, mission-driven team where your contributions have a direct impact on real-world issues.What you’ll do: At TrustLab, your work won’t live in theory - it will power live systems used at large scale. You’ll develop, tune, and optimize LLM-driven solutions that interpret and reason about complex digital content, while experimenting rapidly from design to deployment and seeing immediate feedback from real-world use cases. Partnering closely with other engineers, researchers, and product leaders, you’ll pioneer new approaches to model training and evaluation, taking ownership from early R&D through to production launches, and ensuring your work directly shapes how millions of people experience AI-powered content.Key Responsibilities:Train, evaluate, and monitor new and improved LLMs and other algorithmic modelsTest and deploy content moderation models in production, and iterate based on real-world performance metrics and feedback loops.Develop medium to long-term vision for content understanding-related R&D, working with management, product, policy & operations, and engineering teams.Take ownership of results delivered to customers, pushing for change in approach where needed and taking the lead on cross-functional execution.What we’re looking for:Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Ph.D. is a plus. Proficiency in Python. Experience with AWS and CI/CD processes & tools is a strong plus.Experience with prompt-engineering techniques and familiarity with multiple LLM providers.Several years of industry experience in NLP / Computer Vision, or making LLM’s work in production for non-trivial use cases, incl. familiarity with evaluation metrics for classification tasks and best practices for handling imbalanced datasets.Hands-on experience with debugging issues in production environments, especially on AWS.Strong track record delivering results under time and resource pressureWhy Join Us?Work with a group of renown industry leaders in AI and Online Safety to shape the future of the industry.Ample opportunity and support for growth, as a technical individual contributor, or manager.Apply AI technology to real-world business use cases at a significant scale, with blue chip customersWork as part of a team where you can know everyone, but don’t have to do everyone’s job.Competitive compensation, comprehensive benefits, and hybrid in-office policy.

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About the Team The Knowledge Innovation team is scaling OpenAI with OpenAI. We are building an AI powered knowledge system that evolves and learns as our products, systems and customers evolve. We leverage our state of the art models, technologies, and products (some external, some still in the lab) to assist or completely automate robust operations supporting both internal and external customers. We support OpenAI customers and internal partners globally, powering systems from customer support to integrity to product insights. We are a self-contained multi-disciplinary team, who enjoy a lightning fast feedback loop with customers at scale, some of whom sit just a few pods away. We iterate fast, and engineer for reliable long-term impact. We're constantly looking for the similarities and patterns in different types of work, and focus on building simple primitives, to apply world class knowledge to many domains. The work of this team exemplifies use of OpenAI technologies. We build systems so everyone can see the leverage that is possible with well designed AI-based implementations. We do this by working through internal use cases focused on Customers (specifically knowledge systems, automation systems, and automated agent systems) to prove impact, then we scale. About the RoleWe’re looking for Full Stack Engineers who're passionate about blending production-ready platform architecture with new tech and new paradigms. You’ll push the boundaries of OpenAI’s newest technologies to enable interactions and automations that are not only functional, but delightful. We value proactive, customer-centric engineers who can get the foundational details right (data models, architecture, security) in service of enabling great products. In this role, you will:Own the end-to-end development lifecycle for new platform capabilities and integrations with other systemsCollaborate closely with engineers, data scientists, information systems architects, and internal customers to understand their problems and implement effective solutionsWork with product and research team to share relevant feedback and iterate on applying their latest modelsAbout 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 SunoSuno is a music company built to amplify imagination. Powered by the world’s most advanced AI music model, Suno offers an unparalleled creative platform that includes Suno Studio, a breakthrough generative audio workstation. From shower-singers to aspiring songwriters to seasoned artists, Suno empowers a global community to create, share, and discover music—unlocking the joy of musical expression for all. About the RoleWe're looking for someone to lead recommendations at Suno. You'll be instrumental in building Suno's music discovery and recommendation systems. You'll help define how millions of users discover, create, and engage with music on our platform by shaping both the systems and the team that makes it happen.This role is for someone who has deep experience with recommendation systems at scale and is energized about building a new and better one. You're excited to apply and adapt your expertise in a new context and grow an excellent team to get there.Check out the Suno version of this role here! What You'll DoShape Suno's recommendation vision, strategy, and technical directionPartner with leaders across product, engineering, and research to decide how recommendations evolve with our platformLead the building of a full recommendations system from the ground up, from prototyping and evaluating approaches to experimentation to deploying at scaleBuild and grow a recommendations team What You'll Need5+ years building recommendation systems at scale, with at least 2+ years leading teams and owning the development of recommendation systems in productionDeep technical expertise of what's cutting edge and the ability to get there through practical, iterative stepsStrong collaboration skills and the ability to work with leaders across the company to influence directionPassion for what Suno is building and excitement about defining the future of music discoveryAdditional Notes: Applicants must be eligible to work in the US.This is an onsite role in our SF office Perks & Benefits for Full-Time EmployeesGenerous Company Equity Package401(k) with 3% Employer Match & Roth 401(k)Unlimited PTO & Sick TimeMedical, Dental, & Vision Insurance (PPO w/ HSA & FSA options)Continued / Creative Education StipendGenerous Commuter AllowanceIn-Office Lunch (5 days per week)Suno is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the Massachusetts Fair Chance in Employment Act, NYC Fair Chance Act, LA City Fair Chance Ordinance, and San Francisco Fair Chance Ordinance.

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About CartesiaOur mission is to build the next generation of AI: ubiquitous, interactive intelligence that runs wherever you are. Today, not even the best models can continuously process and reason over a year-long stream of audio, video and text—1B text tokens, 10B audio tokens and 1T video tokens—let alone do this on-device.We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences.We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI.About The RoleTo build truly global AI, our models must be trained on data that reflects the world's diversity of languages and cultures. We are searching for a Machine Learning Engineer to own the quality and coverage of the data behind our models. You will be our in-house expert on global data, ensuring our models perform exceptionally well across dozens of languages. You have a keen eye for linguistic nuance, and a passion for building inclusive and representative datasets at scale.Your ImpactDesign and build large-scale datasets for model training.Build evaluations of speech models, both via manual annotation and at scale with automated metrics.Implement techniques for steering data generation to improve model intelligence through data and mitigate bias.Build automated quality control systems to validate and filter generated dataPartner with product teams to ensure support for key languages and markets.What You BringExperience building or working with large multilingual datasetsExperience with generative models (speech, text, or multimodal).Ability to help guide human annotation and evaluation across multiple languages.Strong applied ML background with a focus on data-centric approaches.Excitement for building scalable systems that bridge research and production.What We Offer🍽 Lunch, dinner and snacks at the office.🏥 Fully covered medical, dental, and vision insurance for employees.🏦 401(k).✈️ Relocation and immigration support.🦖 Your own personal Yoshi.Our Culture🏢 We’re an in-person team based out of San Francisco. We love being in the office, hanging out together, and learning from each other every day.🚢 We ship fast. All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don’t sacrifice quality or design along the way.🤝 We support each other. We have an open & inclusive culture that’s focused on giving everyone the resources they need to succeed.

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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!Why this role?We’re a fast-growing startup building production-grade AI agents for enterprise customers at scale. We’re looking for Applied AI Engineers who can own the design, build, and deployment of agentic workflows powered by Large Language Models (LLMs)—from early prototypes to production-grade AI agents, to deliver concrete business value in enterprise workflows.In this role, you’ll work closely with customers on real-world business problems, often building first-of-their-kind agent workflows that integrate LLMs with tools, APIs, and data sources. While our pace is startup-fast, the bar is enterprise-high: agents must be reliable, observable, safe, and auditable from day one.You’ll collaborate closely with customers, product, and platform teams, and help shape how agentic systems are built, evaluated, and deployed at scale.What You’ll DoWork with enterprise customers and internal teams to turn business workflows into scalable, production-ready agentic AI systems.Design and build LLM-powered agents that reason, plan, and act across tools and data sources with enterprise-grade reliability.Balance rapid iteration with enterprise requirements, evolving prototypes into stable, reusable solutions.Define and apply evaluation and quality standards to measure success, failures, and regressions.Debug real-world agent behavior and systematically improve prompts, workflows, tools, and guardrails.Contribute to shared frameworks and patterns that enable consistent delivery across customers.Required Skills & ExperienceBachelor’s degree in Computer Science or a related technical field.Strong programming skills in Python and/or JavaScript/TypeScript.3+ years of experience building and shipping production software; 2+ years working with LLMs or AI APIs.Hands-on experience with modern LLMs (e.g., GPT, Claude, Gemini), vector databases, and agent/orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex, or custom solutions).Practical experience with RAG, agent workflows, evaluation, and performance optimization.Strong agent design skills, including prompt engineering, tool use, multi-step agent workflows (e.g. ReAct), and failure handling.Ability to reason about and balance trade-offs between customization and reuse, as well as autonomy, control, cost, latency, and risk.Strong communication skills and experience leading technical discussions with customers or partners.Nice-to-HaveExperience working in a fast-moving startup environment.Prior work delivering AI or automation solutions to enterprise customers.Familiarity with human-in-the-loop workflows, fine-tuning, or LLM evaluation techniques.Experience with cloud deployment and production operations for AI systems.Background in applied ML, NLP, or decision systems.Additional RequirementsStrong written and verbal communication skills.Ability and interest to travel up to 25%, flexible.Why Join UsBuild production-grade AI agents used in real enterprise workflows.Operate at scale while retaining end-to-end ownership.Work on hard problems in agent design, evaluation, and reliability.Shape shared platforms and standards, not just individual features.Move fast with a high bar for quality, safety, and reliability.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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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!Why This Role Is DifferentThis is not a typical “Applied Scientist” or “ML Engineer” role. As a Member of Technical Staff, Applied ML, you will:Work directly with enterprise customers on problems that push LLMs to their limits. You’ll rapidly understand customer domains, design custom LLM solutions, and deliver production-ready models that solve high-value, real-world problems.Train and customize frontier models — not just use APIs. You’ll leverage Cohere’s full stack: CPT, post-training, retrieval + agent integrations, model evaluations, and SOTA modeling techniques.Influence the capabilities of Cohere’s foundation models. Techniques, datasets, evaluations, and insights you develop for customers will directly shape the next generation of Cohere’s frontier models.Operate with an early-startup level of ownership inside a frontier-model company. This role combines the breadth of an early-stage CTO with the infrastructure and scale of a deep-learning lab.Wear multiple hats, set a high technical bar, and define what Applied ML at Cohere becomes. Few roles in the industry combine application, research, customer-facing engineering, and core-model influence as directly as this one.What You’ll DoTechnical Leadership & Solution DesignContribute to the design and delivery of custom LLM solutions for enterprise customers.Translate ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies.Modeling, Customization & Foundations ContributionBuild custom models using Cohere’s foundation model stack, CPT recipes, post-training pipelines (including RLVR), and data assets.Develop SOTA modeling techniques that directly enhance model performance for customer use-cases.Contribute improvements back to the foundation-model stack — including new capabilities, tuning strategies, and evaluation frameworks.Customer-Facing Technical ImpactWork as part of Cohere’s customer facing MLE team to identify high-value opportunities where LLMs can unlock transformative impact to our enterprise customers.You May Be a Good Fit If You Have:Technical FoundationsStrong ML fundamentals and the ability to frame complex, ambiguous problems as ML solutions.Fluency with Python and core ML/LLM frameworks.Experience working with (or the ability to learn) large-scale datasets and distributed training or inference pipelines.Understanding of LLM architectures, tuning techniques (CPT, post-training), and evaluation methodologies.Demonstrated ability to meaningfully shape LLM performance.Experience & LeadershipA broad view of the ML research landscape and a desire to push the state of the art.MindsetBias toward action, high ownership, and comfort with ambiguity.Humility and strong collaboration instincts.A deep conviction that AI should meaningfully empower people and organizations.Join UsThis is a pivotal moment in Cohere’s history. As an MTS in Applied ML, you will define not only what we build — but how the world experiences AI. If you're excited about building custom models, solving generational problems for global organizations, and shaping frontier-model capabilities, we’d love to meet you.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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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!Why This Role Is DifferentThis is not a typical “Applied Scientist” or “ML Engineer” role. As a Member of Technical Staff, Applied ML, you will:Work directly with enterprise customers on problems that push LLMs to their limits. You’ll rapidly understand customer domains, design custom LLM solutions, and deliver production-ready models that solve high-value, real-world problems.Train and customize frontier models — not just use APIs. You’ll leverage Cohere’s full stack: CPT, post-training, retrieval + agent integrations, model evaluations, and SOTA modeling techniques.Influence the capabilities of Cohere’s foundation models. Techniques, datasets, evaluations, and insights you develop for customers will directly shape the next generation of Cohere’s frontier models.Operate with an early-startup level of ownership inside a frontier-model company. This role combines the breadth of an early-stage CTO with the infrastructure and scale of a deep-learning lab.Wear multiple hats, set a high technical bar, and define what Applied ML at Cohere becomes. Few roles in the industry combine application, research, customer-facing engineering, and core-model influence as directly as this one.What You’ll DoTechnical Leadership & Solution DesignLead the design and delivery of custom LLM solutions for enterprise customers.Translate ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies.Modeling, Customization & Foundations ContributionBuild custom models using Cohere’s foundation model stack, CPT recipes, post-training pipelines (including RLVR), and data assets.Develop SOTA modeling techniques that directly enhance model performance for customer use-cases.Contribute improvements back to the foundation-model stack — including new capabilities, tuning strategies, and evaluation frameworks.Customer-Facing Technical ImpactWork closely with enterprise customers to identify high-value opportunities where LLMs can unlock transformative impact.Provide technical leadership across discovery, scoping, modeling, deployment, agent workflows, and post-deployment iteration.Establish evaluation frameworks and success metrics for custom modeling engagements.Team Mentorship & Organizational ImpactMentor engineers across distributed teams.Drive clarity in ambiguous situations, build alignment, and raise engineering and modeling quality across the organization.You May Be a Good Fit If You Have:Technical FoundationsStrong ML fundamentals and the ability to frame complex, ambiguous problems as ML solutions.Fluency with Python and core ML/LLM frameworks.Experience working with large-scale datasets and distributed training or inference pipelines.Understanding of LLM architectures, tuning techniques (CPT, post-training), and evaluation methodologies.Demonstrated ability to meaningfully shape LLM performance.Experience & LeadershipExperience engaging directly with customers or stakeholders to design and deliver ML-powered solutions.A track record of technical leadership at a team level.A broad view of the ML research landscape and a desire to push the state of the art.MindsetBias toward action, high ownership, and comfort with ambiguity.Humility and strong collaboration instincts.A deep conviction that AI should meaningfully empower people and organizations.Join UsThis is a pivotal moment in Cohere’s history. As an MTS in Applied ML, you will define not only what we build — but how the world experiences AI. If you're excited about building custom models, solving generational problems for global organizations, and shaping frontier-model capabilities, we’d love to meet you.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 Sana Sana is an AI lab building superintelligence for work. We believe organizations can accomplish their missions faster when humans can effortlessly access knowledge, automate repetitive work, and learn anything with the help of agentic AI. As part of Workday, we are committed to building AI that augments humans. If that is a mission that excites you, you are in the right place.About the rolesYou will work on the core backend systems that power Sana’s Learn platform and search infrastructure. You will redesign and scale systems to handle enterprise workloads, remove deep bottlenecks, and evolve our architecture for the era of AI and agents.We are hiring for two senior roles:Senior Backend Engineer, Learn (Core Systems)Senior Backend Engineer, Search1. Senior Backend Engineer, Learn (Core Systems)About the roleYou will focus on making the Learn platform, especially Manage, Insights, and APIs, scalable and maintainable for enterprise use. You will take systems that work at current scale and redesign them to handle much larger volumes of data, users, and entities. The work is primarily architectural and system level, with strong emphasis on Postgres and database design.In this role, you willRedesign existing components to support enterprise scale workloadsAnalyze and resolve bottlenecks in storage, query performance, APIs, and data modelsLead migrations away from legacy implementations to sustainable replacementsImprove reliability and efficiency of APIs and integrations for internal and external clientsDrive technical projects from definition to delivery with Product Managers and other teamsMaintain a long term view of system health and architectureShare technical knowledge, review designs, and set best practices for backend and systems designWhat success looks likeArchitecture and scalability are prioritized over short term fixesRisks are identified early and addressed with solid technical solutionsYou balance hands on coding with architectural leadershipLearn core systems support larger enterprise customers without performance or reliability issuesStrong Postgres skills (schema design, query optimization, indexing) improve performance and scalability2. Senior Backend Engineer, SearchAbout the roleYou will build and scale Sana’s search infrastructure for enterprise scale knowledge discovery. You will architect retrieval systems, ranking algorithms, and search APIs that handle large increases in volume while maintaining sub second latency and high relevance. You will rethink the search stack for the era of agents, using engines like Vespa, vector databases, and embedding models.In this role, you willArchitect and scale search infrastructure to billions of documents in multi tenant environmentsDesign hybrid search that combines keyword search with semantic understanding and vector searchBuild ranking and personalization systems that learn from user behaviorCollaborate with AI engineers to integrate large language models into the search pipeline and build retrieval augmented systemsOptimize search performance across query parsing, index design, and distributed architectureLead development of search observability and quality frameworks with clear metrics and monitoringWork closely with product and design to shape the future of knowledge discovery at SanaWhat success looks likeSearch systems scale elegantly to billions of documents and millions of queries with strict latency targetsSystems apply proven IR techniques and practical advances in semantic AI retrievalSearch quality issues are detected early through strong observability and automated regression checksYou balance implementation with system design and mentor others on search and IR fundamentalsDeep expertise in search infrastructure (index optimization, query planning, distributed retrieval, caching) prevents performance issues before users see themThe stack evolves from traditional search to AI powered discovery, including embeddings, reranking, and RAG, while staying reliableSearch infrastructure supports enterprise deployments with strict SLAs, multi tenancy isolation, and very high uptimeWhat we offerHelp shape AI's future alongside brilliant minds from Notion, Dropbox, Slack, Databricks, Google, McKinsey, and BCG.Competitive salary and compensation packageSwift professional growth in an evolving environment, supported by a culture of continuous feedback and mentorship from senior leaders.Work with talented teammates across 5+ countries, and collaborate with customers globallyRegular team gatherings and events (recently in Italy and South Africa)

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About SesameSesame believes in a future where computers are lifelike - with the ability to see, hear, and collaborate with us in ways that feel natural and human. With this vision, we're designing a new kind of computer, focused on making voice companions part of our daily lives. Our team brings together founders from Oculus and Ubiquity6, alongside proven leaders from Meta, Google, and Apple, with deep expertise spanning hardware and software. Join us in shaping a future where computers truly come alive.About the RoleAs a Backend Infrastructure Engineer at Sesame, you will work across the stack, with an emphasis on backend, infrastructure, and systems. You should feel comfortable working on any typical software project, with the ability to quickly learn new tools and services with a strong intuition on software development and software deployment best practices.Responsibilities: Build a secure, maintainable, self-serve core infrastructure.Build a modern training architecture and a modern serving architecture.Build and scale a low-latency voice interface and audio processing pipeline.Build low maintenance, high leverage developer tooling, server, and data infrastructure.Required Qualifications:Ability to set, own, and communicate direction.  Hands-on reliability engineering experience. You hold convictions about monitoring and observability methodology, deployment systems, loosely coupled components, etc.Experience shipping services at scale.Experienced systems designer in designing and shipping flexible domain models and APIs.A track record of delivering efficiency through automation.Experience solving challenging infrastructure problems (using the tools below). Preferred Qualifications:Significant IaC experience (Terraform preferred).CloudFormation, Pulumi, Kube-based, etc. Architected a multi-stack, self-serve Terraform system, or maintained one.Understand the challenges of building infrastructure at scale.Significant Kubernetes experience.Some ML experience.Torch experience, especially model optimization for serving.General ML training or serving experience. Built ML serving and/or training infrastructure.Experience in Torch Serve, Seldon, KServe, Ray Serve, etc.Experience building large-scale distributed training and serving systems.Data engineering experience.Technical leadership.Database design.Real-time communication experience.Sesame is committed to a workplace where everyone feels valued, respected, and empowered. We welcome all qualified applicants, embracing diversity in race, gender, identity, orientation, ability, and more. We provide reasonable accommodations for applicants with disabilities—contact careers@sesame.com for assistance.Full-time Employee Benefits: 401k matching100% employer-paid health, vision, and dental benefits Unlimited PTO and sick time Flexible spending account matching (medical FSA) Benefits do not apply to contingent/contract workers

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About Us:webAI is pioneering the future of artificial intelligence by establishing the first distributed AI infrastructure dedicated to personalized AI. We recognize the evolving demands of a data-driven society for scalability and flexibility, and we firmly believe that the future of AI lies in distributed processing at the edge, bringing computation closer to the source of data generation. Our mission is to build a future where a company's valuable data and intellectual property remain entirely private, enabling the deployment of large-scale AI models directly on standard consumer hardware without compromising the information embedded within those models. We are developing an end-to-end platform that is secure, scalable, and fully under the control of our users, empowering enterprises with AI that understands their unique business. We are a team driven by truth, ownership, tenacity, and humility, and we seek individuals who resonate with these core values and are passionate about shaping the next generation of AI.About the Role:The AI Research Scientist will contribute to webAI’s development of next-generation AI models and systems. In this role, you will design, train, evaluate, and optimize cutting-edge machine learning models including large language models, multimodal architectures, and on-device inference systems. You will work closely with research leadership, applied AI teams, and platform engineering to advance scientific discovery while ensuring that innovations translate into real-world impact.This is a hands-on research role for someone who loves experimentation, solving complex problems, and building AI that is powerful, efficient, and privacy-preserving. Responsibilities:Design, train, and optimize machine learning models including LLMs, multimodal models, transformers, and diffusion architecturesConduct research on model efficiency, quantization, compression, and on-device deploymentPrototype novel model architectures, training methods, and inference strategies for distributed AIDevelop and evaluate benchmarks, datasets, and experimental frameworks to test model performanceCollaborate with engineering teams to integrate research findings into production systemsStay current on leading research in deep learning, generative AI, and distributed MLAnalyze experimental results and communicate insights clearly to technical and non-technical stakeholdersDocument research findings, contribute to internal papers, and present technical work across the organizationIdentify emerging technologies and propose research directions aligned with webAI’s strategic priorities Qualifications:4+ years of experience (can be graduate research) in machine learning research, AI model development, or related fieldsStrong expertise in deep learning architectures including transformers, CNNs, RNNs, and diffusion modelsHands-on experience training and fine-tuning large-scale modelsProficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAXExperience building datasets, designing experiments, and validating ML model performanceDeep understanding of optimization techniques including quantization, distillation, pruning, and hardware-aware trainingStrong problem-solving skills and ability to work independently on complex research tasksEffective communication skills for presenting research findings to diverse audiencesBachelor’s degree in Computer Science, Engineering, Mathematics, or a related field Preferred SkillsMaster’s or PhD in Machine Learning, Computer Science, AI, or a related fieldExperience with distributed training, edge inference, or on-device MLResearch experience in generative AI, reinforcement learning, or multimodal learningFamiliarity with privacy-preserving ML techniques such as federated learningExperience contributing to academic publications, patents, or open-source ML projectsComfort operating in a fast-paced, high-growth startup environment We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following:Truth - Emphasizing transparency and honesty in every interaction and decision.Ownership - Taking full responsibility for one’s actions and decisions, demonstrating commitment to the success of our clients. Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement.Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others.Benefits:Competitive salary and performance-based incentives.Comprehensive health, dental, and vision benefits package.401k Match (US-based only)$200/mos Health and Wellness Stipend$400/year Continuing Education Credit$500/year Function Health subscription (US-based only)Free parking, for in-office employeesUnlimited Approved PTOParental Leave for Eligible EmployeesSupplemental Life Insurance webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.

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About the role We’re hiring an AI Architect to sit at the intersection of frontier AI research, product, and go-to-market. You’ll partner closely with ML teams in high-stakes meetings, scope and pitch solutions to top AI labs, and translate research needs (post-training, evals, alignment) into clear product roadmaps and measurable outcomes. You’ll drive end-to-end delivery—partnering with AI research teams and core customers to scope, pilot, and iterate on frontier model improvements—while coordinating with engineering, ops, and finance to translate cutting-edge research into deployable, high-impact solutions. What you’ll do Translate research → product: work with client side researchers on post-training, evals, safety/alignment and build the primitives, data, and tooling they need. Partner deeply with core customers and frontier labs: work hands-on with leading AI teams and frontier research labs to tackle hard, open-ended technical problems related to frontier model improvement, performance, and deployment. Shape and propose model improvement work: translate customer and research objectives into clear, technically rigorous proposals—scoping post-training, evaluation, and safety work into well-defined statements of work and execution plans. Translate research into production impact: collaborate with customer-side researchers on post-training, evaluations, and alignment, and help design the data, primitives, and tooling required to improve frontier models in practice. Own the end-to-end lifecycle: lead discovery, write crisp PRDs and technical specs, prioritize trade-offs, run experiments, ship initial solutions, and scale successful pilots into durable, repeatable offerings. Lead complex, high-stakes engagements: independently run technical working sessions with senior customer stakeholders; define success metrics; surface risks early; and drive programs to measurable outcomes. Partner across Scale: collaborate closely with research (agents, browser/SWE agents), platform, operations, security, and finance to deliver reliable, production-grade results for demanding customers. Build evaluation rigor at the frontier: design and stand up robust evaluation frameworks (e.g., RLVR, benchmarks), close the loop with data quality and feedback, and share learnings that elevate technical execution across accounts. You have Deep technical background in applied AI/ML: 5–10+ years in research, engineering, solutions engineering, or technical product roles working on LLMs or multimodal systems, ideally in high-stakes, customer-facing environments. Hands-on experience with model improvement workflows: demonstrated experience with post-training techniques, evaluation design, benchmarking, and model quality iteration. Ability to work on hard, ambiguous technical problems: proven track record of partnering directly with advanced customers or research teams to scope, reason through, and execute on deep technical challenges involving frontier models. Strong technical fluency: you can read papers, interrogate metrics, write or review complex Python/SQL for analysis, and reason about model-data trade-offs. Executive presence with world-class researchers and enterprise leaders; excellent writing and storytelling. Bias to action: you ship, learn, and iterate. How you’ll work Customer-obsessed: start from real research needs; prototype quickly; validate with data. Cross-functional by default: align research, engineering, ops, and GTM on a single plan; communicate clearly up and down. Field-forward: expect regular customer time and research leads; light travel as needed. What success looks like Clear wins with top labs: pilots that convert to scaled programs with strong eval signals. Reusable alignment & eval building blocks that shorten time-to-value across accounts. Crisp internal docs (PRDs, experiment readouts, exec updates) that drive decisions quickly. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:$190,000—$230,000 USDPLEASE 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 MercorMercor is at the intersection of labor markets and AI research. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development.Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $1.5 million a day.Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society.Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our new San Francisco headquarters.About the RoleAs a Research Engineer at Mercor, you’ll work at the intersection of engineering and applied AI research. You’ll contribute directly to post-training and RLVR, synthetic data generation, and large-scale evaluation workflows that meaningfully impact frontier language models.Your work will be used to train large language models to master tool use, agentic behavior, and real-world reasoning in real-world production environments. You’ll shape rewards, run post-training experiments, and build scalable systems that improve model performance. You’ll help design and evaluate datasets, create scalable data augmentation pipelines, and build rubrics and evaluators that push the boundaries of what LLMs can learn.What You’ll DoWork on post-training and RLVR pipelines to understand how datasets, rewards, and training strategies impact model performance.Design and run reward-shaping experiments and algorithmic improvements (e.g., GRPO, DAPO) to improve LLM tool-use, agentic behavior, and real-world reasoning.Quantify data usability, quality, and performance uplift on key benchmarks.Build and maintain data generation and augmentation pipelines that scale with training needs.Create and refine rubrics, evaluators, and scoring frameworks that guide training and evaluation decisions.Build and operate LLM evaluation systems, benchmarks, and metrics at scale.Collaborate closely with AI researchers, applied AI teams, and experts producing training data.Operate in a fast-paced, experimental research environment with rapid iteration cycles and high ownership.What We’re Looking ForStrong applied research background, with a focus on post-training and/or model evaluation.Strong coding proficiency and hands-on experience working with machine learning models.Strong understanding of data structures, algorithms, backend systems, and core engineering fundamentals.Familiarity with APIs, SQL/NoSQL databases, and cloud platforms.Ability to reason deeply about model behavior, experimental results, and data quality.Excitement to work in person in San Francisco, five days a week (with optional remote Saturdays), and thrive in a high-intensity, high-ownership environment.Nice To HaveReal-world post-training team experience in industry (highest priority).Publications at top-tier conferences (NeurIPS, ICML, ACL).Experience training models or evaluating model performance.Experience in synthetic data generation, LLM evaluations, or RL-style workflows.Work samples, artifacts, or code repositories demonstrating relevant skills.BenefitsGenerous equity grant vested over 4 yearsA $20K relocation bonus (if moving to the Bay Area)A $10K housing bonus (if you live within 0.5 miles of our office)A $1K monthly stipend for mealsFree Equinox membershipHealth insurance