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🚀 About WRITERWRITER is where the world's leading enterprises orchestrate AI-powered work. Our vision is to expand human capacity through superintelligence. And we're proving it's possible – through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise-wide transformation. With WRITER's end-to-end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise-grade LLMs. Valued at $1.9B and backed by industry-leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI.Founded in 2020 with office hubs in San Francisco, New York City, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI.📐 About the roleAs a deployment engineer at WRITER, you'll be at the forefront of expanding human capacity through superintelligence. This isn't just a technical role; it's a deeply influential one where you'll partner directly with our leading enterprise customers. Your expertise will be crucial in uncovering their unique business challenges and architecting AI-powered solutions that leverage our powerful platform and enterprise-grade LLMs. You'll transform complex needs into tangible, high-impact applications, creating champions and driving tangible business results. Your builder's mentality and passion for bringing cutting-edge AI into the hands of real users will directly shape the future of work for some of the world's largest companies.This is a critical role that directly impacts our customers' success and product evolution. You'll contribute significantly to WRITER's mission, working with a dynamic team to push the boundaries of what's possible with generative AI.This is a hybrid role based out of our San Francisco, Chicago, Austin, and New York City hubs.🦸🏻‍♀️ What you'll doPartner deeply with enterprise customers to identify strategic AI use cases, validating technical feasibility and owning the end-to-end implementation of tailored solutionsArchitect and deliver custom applications, templates, and integrations leveraging WRITER's platform, APIs, and Knowledge Graph capabilities to solve complex business challengesTranslate intricate technical concepts and platform capabilities into clear, prescriptive solution recommendations, guiding customers through the generative AI landscapeCollaborate relentlessly with internal Product and Engineering teams, providing crucial customer feedback that directly influences our product roadmap and drives continuous innovationDrive down customer time-to-value by developing scalable processes, robust documentation, and efficient workflows for technical integrationsChampion the successful adoption and expansion of WRITER's AI solutions within customer accounts, ensuring maximum impact and return on investment⭐️ What you needOver 5 years of hands-on experience in technical SaaS roles, with at least 3 years specifically in solutions architecture, technical consulting, or technical account managementDeep technical fluency in Python and extensive experience leveraging APIs, especially in designing sophisticated prompts and understanding linguistic principles for AI applicationsProven track record of architecting and delivering complex technical solutions for Fortune 500 enterprises in a fast-paced, high-growth B2B SaaS environmentExceptional communication and executive presence, capable of simplifying complex AI concepts for diverse audiences, from technical teams to C-suite leadershipA true builder's mindset with a passion for emerging technologies, always eager to experiment, learn, and iterate in the rapidly evolving world of generative AIA relentless drive to Connect with customers and internal teams, Challenge the status status quo with innovative solutions, and Own the successful outcomes for our most strategic accounts 🍩 Benefits & perks (US Full-time employees)Generous PTO, plus company holidaysMedical, dental, and vision coverage for you and your familyPaid parental leave for all parents (16 weeks)Fertility and family planning supportEarly-detection cancer testing through GalleriFlexible spending account and dependent FSA optionsHealth savings account for eligible plans with company contributionAnnual work-life stipends for:Wellness stipend for gym, massage/chiropractor, personal training, etc.Learning and development stipendCompany-wide off-sites and team off-sitesCompetitive compensation, company stock options and 401kWRITER is an equal-opportunity employer and is committed to diversity. We don't make hiring or employment decisions based on race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other basis protected by applicable local, state or federal law. Under the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.By submitting your application on the application page, you acknowledge and agree to WRITER's Global Candidate Privacy Notice.

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🚀 About WRITERWRITER is where the world's leading enterprises orchestrate AI-powered work. Our vision is to expand human capacity through superintelligence. And we're proving it's possible – through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise-wide transformation. With WRITER's end-to-end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise-grade LLMs. Valued at $1.9B and backed by industry-leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI.Founded in 2020 with office hubs in San Francisco, New York City, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI.📐 About the roleAs a deployment engineer at WRITER, you'll be at the forefront of expanding human capacity through superintelligence. This isn't just a technical role; it's a deeply influential one where you'll partner directly with our leading enterprise customers. Your expertise will be crucial in uncovering their unique business challenges and architecting AI-powered solutions that leverage our powerful platform and enterprise-grade LLMs. You'll transform complex needs into tangible, high-impact applications, creating champions and driving tangible business results. Your builder's mentality and passion for bringing cutting-edge AI into the hands of real users will directly shape the future of work for some of the world's largest companies.This is a critical role that directly impacts our customers' success and product evolution. You'll contribute significantly to WRITER's mission, working with a dynamic team to push the boundaries of what's possible with generative AI.This is a hybrid role based out of our San Francisco, Chicago, Austin, and New York City hubs.🦸🏻‍♀️ What you'll doPartner deeply with enterprise customers to identify strategic AI use cases, validating technical feasibility and owning the end-to-end implementation of tailored solutionsArchitect and deliver custom applications, templates, and integrations leveraging WRITER's platform, APIs, and Knowledge Graph capabilities to solve complex business challengesTranslate intricate technical concepts and platform capabilities into clear, prescriptive solution recommendations, guiding customers through the generative AI landscapeCollaborate relentlessly with internal Product and Engineering teams, providing crucial customer feedback that directly influences our product roadmap and drives continuous innovationDrive down customer time-to-value by developing scalable processes, robust documentation, and efficient workflows for technical integrationsChampion the successful adoption and expansion of WRITER's AI solutions within customer accounts, ensuring maximum impact and return on investment⭐️ What you needOver 5 years of hands-on experience in technical SaaS roles, with at least 3 years specifically in solutions architecture, technical consulting, or technical account managementDeep technical fluency in Python and extensive experience leveraging APIs, especially in designing sophisticated prompts and understanding linguistic principles for AI applicationsProven track record of architecting and delivering complex technical solutions for Fortune 500 enterprises in a fast-paced, high-growth B2B SaaS environmentExceptional communication and executive presence, capable of simplifying complex AI concepts for diverse audiences, from technical teams to C-suite leadershipA true builder's mindset with a passion for emerging technologies, always eager to experiment, learn, and iterate in the rapidly evolving world of generative AIA relentless drive to Connect with customers and internal teams, Challenge the status status quo with innovative solutions, and Own the successful outcomes for our most strategic accounts 🍩 Benefits & perks (US Full-time employees)Generous PTO, plus company holidaysMedical, dental, and vision coverage for you and your familyPaid parental leave for all parents (16 weeks)Fertility and family planning supportEarly-detection cancer testing through GalleriFlexible spending account and dependent FSA optionsHealth savings account for eligible plans with company contributionAnnual work-life stipends for:Wellness stipend for gym, massage/chiropractor, personal training, etc.Learning and development stipendCompany-wide off-sites and team off-sitesCompetitive compensation, company stock options and 401kWRITER is an equal-opportunity employer and is committed to diversity. We don't make hiring or employment decisions based on race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other basis protected by applicable local, state or federal law. Under the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.By submitting your application on the application page, you acknowledge and agree to WRITER's Global Candidate Privacy Notice.

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Think Different. Build the Future. 🚀Our MissionBuild everyday AGI. Trustworthy, consumer-grade agents that redefine human–AI collaboration for millions. Software shouldn’t wait for commands; it should partner with you, amplifying what you can do every single day.Why AGI, Inc.We’re a stealth team of elite founders and AI researchers, with backgrounds spanning Stanford, OpenAI, and DeepMind. We’re industry leaders in mobile and computer-use agents, bringing these capabilities to consumer scale.Grounded in years of agent research, our AI is designed with trustworthiness and reliability as core pillars, not afterthoughts. We are supported by tier-1 investors who funded the first generation of AI giants; now they’re backing us to build the next: everyday AGI. (Watch the demo)If you see possibility where others see limits, read on.What You’ll DoTraining Automation: Design and implement robust CI/CD pipelines for machine learning workflows. Automate nightly and on-demand training runs, including data ingestion, job orchestration, checkpointing, and artifact management, with reliability as a first-class requirement.Evaluation Infrastructure: Build scalable evaluation harnesses that automatically benchmark models on every merge. Optimize latency and resource usage so experimentation stays fast, and performance regressions are caught immediately.Research Tooling: Develop internal SDKs, CLIs, and lightweight UIs (e.g., Streamlit, Retool) that empower researchers to:Inspect trajectories and tracesVisualize model failuresCurate and manage datasetsIterate without frictionYou’ll make experimentation ergonomic.Observability & Performance: Implement comprehensive tracking for:Model latency, throughput, and error ratesGPU utilization and cluster healthInference cost and unit economicsBuild dashboards and alerting systems that give real-time visibility into system performance and reliability.Minimum QualificationsBachelor’s degree in Computer Science, Engineering, or equivalent practical experience3+ years in Software Engineering, MLOps, or ML InfrastructureStrong Python proficiencyExperience building internal developer tools, CLIs, or dashboardsExperience with cloud infrastructure (AWS or GCP) and containerization (Docker, Kubernetes)Preferred QualificationsExperience designing CI/CD pipelines specifically for ML workflowsFamiliarity with LLM serving stacks such as vLLM or TGIExperience managing GPU clusters and optimizing distributed workloadsWhy This Role MattersGreat research without great infrastructure slows to a crawl. Great infrastructure multiplies the impact of every researcher.You will define how experiments scale, how reliability is measured, and how quickly we can ship improvements to real users. The systems you build will directly shape the speed and quality of our progress toward everyday AGI.Our Culture🏢 All in, in person — work moves faster face-to-face 🚀 Ship by default — novel and polished can coexist, speed is the feature 🤝 One band, one sound — radical candor, zero politics, help each other winPerks🏥 Competitive company-sponsored medical, dental, and vision insurance ✈️ Top-tier relocation and immigration supportHow to Apply Send us:A link — or 60-second video — of something you built and why it mattersYour resume or LinkedInTwo sentences on the hardest problem you've crackedEvery exceptional candidate hears back within 48 hours. If you see possibility where others see limits, we'd love to meet you.

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About Beacon AIWe’re a fast-moving team of aviators, engineers, and operators building an AI platform to make flying safer, more efficient, and more capable. Backed by top investors, we’ve secured a dozen Department of Defense contracts and partnered with major airlines to deliver mission-critical systems. We operate without silos or heavy processes. Small, focused teams own what they build, ship quickly, and learn fast, pushing the boundaries of how humans and AI work together in aviation.You will ship LLM-powered product features end-to-end. That means designing retrieval and tool-calling flows, writing the services that run them, building evals and guardrails, and watching cost, latency, and quality in production. You’ll partner with the ML/infra teammates on embeddings, indexing, and model hosting, and with the product teammates on user experience and outcomes. We move fast, and we care about reliability in a safety-critical domain.We’re hiring across levels. Senior engineers own features and services. Staff engineers own systems, standards, and cross-team technical direction.What you’ll doBuild user-facing LLM featuresDesign and implement retrieval-augmented generation and tool-calling flows using frameworks like LangChain or equivalent primitives, where simpler is better.Deliver robust JSON and schema-bound outputs with validation, retries, and fallbacks.Add function calling to integrate with internal tools, search, routing, and data services.Own the service layerShip APIs and workers in Python or TypeScript with clear contracts, streaming, and backoff.Add caching, request shaping, prompt templates, and context packing to control latency and cost.Integrate with AWS Bedrock, OpenAI, Anthropic, or self-hosted endpoints as needed.Retrieval and data prepCollaborate with infrastructure teammates to develop chunking, embeddings, and indexing capabilities for documents, time series, and multimedia.Choose and tune vector backends such as OpenSearch, pgvector, or Pinecone.Keep knowledge bases fresh with data syncs from S3, Aurora, DynamoDB, and external sources.Evaluation and qualityCreate offline evals and golden sets for prompts, retrievers, and tools.Stand up online metrics for task success, hallucination rate, retrieval precision/recall, p95 latency, and cost per request.Run A/B tests and prompt/version rollouts with guardrails and canaries.Safety, privacy, and complianceImplement content and policy checks, PII detection and redaction, access controls, and auditing.Design human-in-the-loop paths for sensitive actions.Handle aviation data with care and follow internal security standards.Operate what you buildAdd tracing, logs, and dashboards for model calls, token usage, errors, and saturation.Debug tricky failures across retrieval, prompts, tools, and providers.What will make you successfulShipped LLM apps: You’ve put LLM features in front of users and improved them with data.Strong builder: Comfortable writing production code, tests, and docs. You keep things simple and observable.RAG and tools depth: You understand embeddings, chunking, vector search tradeoffs, and function calling.Quality mindset: You design evals, define success metrics, and iterate based on evidence.Cost and latency aware: You track p95, hit SLAs, and reduce cost without hurting quality.Clear communicator: You explain tradeoffs and align partners across product, infra, and security.Nice to haveExperience with Bedrock, OpenSearch Serverless, pgvector, Pinecone, or Weaviate.Prompt versioning, guardrails, and provider routing in production.Multimodal work with time series or video.Familiarity with GPU inference, Triton, or TensorRT-LLM.Aviation or other safety-critical domain exposure.DevOps basics for CI/CD, IaC, and secure secrets handling.Example problems you might tackle in month oneTransform an internal knowledge base into a low-latency RAG service, complete with explicit schemas and evaluations.Add tool-calling to automate a repetitive cockpit or ops workflow with guardrails and audit trails.Reduce the cost per request through improved chunking, caching, and prompt refactoring, while maintaining task success rates.Work Location This is a hybrid role based in San Carlos, CA, with 3+ days per week onsite and the option to work remotely on remaining days.Perks & Benefits (Full-Time Employees)Healthcare: 100%* of employee medical premiums covered; 25% for dependentsTime Off: 3 weeks PTO plus 13+ paid company holidaysStipends: Monthly phone and wellness benefits401(k): Offered (no current employer match, but we are committed to enhancing this benefit in the future).Due to U.S. export control regulations, we can only hire U.S. Persons (U.S. citizens, Green Card holders, lawful permanent residents, or individuals granted asylum or refugee status). We are unable to provide visa sponsorship or support visa transfers. All work must be performed in the United States. Beacon AI is an equal opportunity employer and does not discriminate based on race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected characteristic. We prohibit harassment or discrimination of any kind in the workplace and comply with all applicable federal, state, and local employment laws.

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7AI empowers Security teams to shift high-value tasks to intelligent AI agents that help reshape the future of cybersecurity and automation. We’re building at the bleeding edge of AI, blending deep engineering with practical product impact. You’ll collaborate with mission-driven teams in a fast-paced, high-growth environment where your contributions directly influence what’s next in AI-powered systems.This role builds on, but is distinct from, traditional ML engineering: the focus is not on training models from scratch, but on composing, optimizing, and scaling AI systems that solve complex enterprise problems.What You’ll DoArchitect and build LLM-powered systems — design retrieval workflows, context management, agent prompts, and structured output pipelines.Orchestrate AI workflows using tools like LangChain, LlamaIndex, or similar frameworks, integrating them with product APIs and backend services.Drive prompt engineering and iteration — refine prompts, templates, and context strategies to meet product quality and reliability goals.Manage real-world evaluation metrics — measure usefulness, factual correctness, latency, and UX impact vs. classic accuracy alone.Collaborate across functions — work closely with product, platform, and backend teams to ensure seamless integration.Develop reliable, scalable deployments — focus on performance, cost efficiency, and observability in production environments.Who You AreExperienced in building real LLM applications — you’ve shipped systems that use large models meaningfully.Strong software engineering skills — Python/TypeScript, API design, backend integration, and cloud deployment.Tool fluency — comfortable with RAG, vector databases (e.g., Pinecone/Weaviate), workflow frameworks (LangChain, Dust), and related tooling.Architectural thinker — you can diagram end-to-end solutions incorporating context windows, caching strategies, tool calls, and multi-step reasoning.Product-oriented — you care not just that the AI works, but that it delivers value safely and reliably to users.Basic Qualifications6+ years of software engineering experience, with at least 1+ year working building AI in production.BS Degree in Computer Science or related field. Masters degree is a plus.Demonstrated use of LLMs in production workflows or complex prototypes.Strong coding ability in Python or equivalent; familiarity with backend frameworks and cloud services.Experience with API integrations, database systems, and scalable architectures.Experience with multi-modal models or multi-agent system design.Familiarity with AI safety guardrails, hallucination mitigation, and structured output enforcement.Knowledge of vector DBs, RAG architectures, and prompt lifecycle tooling.

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About Us:Articul8 was born from a simple belief: GenAI should work for the enterprise, not the other way around. Our platform — combining domain-specific models, autonomous agentic reasoning (ModelMesh™), reliable model evaluation (LLM-IQ™), and multimodal understanding — serves regulated industries such energy, semiconductor, finance, aerospace, supply chain, and more. Trusted by Fortune 500 enterprises, we bring together research, engineering, product, and domain expertise to deliver AI that meets the accuracy, explainability, and auditability standards that high-stakes environments demand.Job Description: Articul8 AI is seeking an exceptional Product/Software Engineer-Backend to join us in shaping the future of Generative Artificial Intelligence (GenAI). We are looking for a Product/Software Engineer-Backend with a proven track record of designing and building scalable production-level software. As a member of our Product Technology team, you will play a critical role in designing, developing, and maintaining the backend software and infrastructure supporting our GenAI-powered products. You'll collaborate with cross-functional teams to drive innovation, optimize performance, and foster growth. This position offers exciting opportunities to work closely with cross-functional teams and external partners to drive innovations in enterprise-grade GenAI. Responsibilities: Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems, focusing on high availability, low latency, and cost-effectiveness. Be the subject matter expert in infrastructure when designing new products and introducing new technology to our existing product line. Collaborate closely with engineering and research teams to integrate infrastructure components with product features, ensuring optimal system performance and user experience. Design event-driven architectures and develop APIs and microservices to support real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling mechanisms. Stay up-to-date with emerging trends, technologies, and methodologies, applying this knowledge to enhance our infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers. Required Qualifications: Professional experience: 7+ years of design, implementation, or consulting in applications and backend software development experience. Education: BSc degree in Computer Science, Engineering, or a related field. Preferred Qualifications: Technology stack: Cloud Platforms: AWS, Azure, GCP Programming/ Development: Python, Node.js, Go, Ruby, CI/CD pipelines, and version control. Frameworks: Django, Flask, OpenAPI, FastAPI, Spring Boot, Express.js Databases: PostgreSQL, MySQL, MongoDB, Redis API Management: REST, GraphQL. Container Orchestration: Kubernetes, Docker Swarm Education: Master’s or PhD in Computer science or related technical fields. Professional Attributes (Code42):Practice Humility: You ask questions even when you think you know the answer. You seek feedback early, learn from anyone regardless of title, and treat every experiment — especially the failures — as data.Bias for Outcomes: You measure your work by what changed, not what you tried. You ship results, not slide decks. When a deadline is real, you find a way.Care Deeply: You treat every problem as yours to solve. You review your own work with the rigor you'd want from a reviewer. You help teammates without being asked.Dare to Do the Impossible & Embrace Scarcity: You set goals that make you uncomfortable. When told something can't be done, you find a way or a better question. Constraints sharpen your thinking, not slow it down.Build a Better World: You believe AI should make things meaningfully better for real people. You hold yourself accountable not just for whether your model works, but for what it does in the world.

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About Brain Co.Brain Co. is an applied AI startup co-founded by Jared Kushner and Elad Gil, and backed by leading Silicon Valley builders including Patrick Collison and Andrej Karpathy. We are building AI applications for the world’s most important institutions, delivering impact on real-world problems across governments, healthcare systems, and critical industries. Our progress so far:Automated construction permitting for a sovereign government → 80% faster, unlocking $375M+ in valueOptimized supply chains for a leading global energy company → 30% lower cost, 99% reliability, preventing $100M+ in lossesStreamlined hospital patient care across national health systems → 40% better outcomes, 80% less admin workCompany momentum:Raised a $55M Series A from leading investorsBuilt a team of 70+ AI experts from Tesla, Google DeepMind, NVIDIA, and DatabricksAt Brain Co., we focus on applying frontier AI to real institutional challenges, working alongside governments, healthcare systems, and critical industries to modernize how essential services operate. We are looking for leaders who want to help bring new technology into institutions that impact millions of people.About the role:You'll join the team that builds and enables agentic workflows across Brain Co. For every engineer, operator, and business team internally, and for the production AI systems we deploy to governments, healthcare systems, and critical industries. This is a platform role at the center of the company's agent-first strategy: you'll build foundational systems used by every engineering team, and the bar is product-grade because the entire company depends on them.What you’ll work on:Own the foundations of how LLMs are used across the company: cost visibility and controls, data privacy, identity and access, routing, and the security posture around all provider traffic.Design the sandboxing, orchestration, audit, and guardrail layers that product teams build their agents on, so verticals don't need to invent their own abstraction.Solve the hard problems: prompt-injection defenses, scoped credentials, kill switches, multi-tenant isolation (including VM-level pod isolation), and runaway-cost controls.Design the orchestration, isolation, and resource models that make this viable: cold-start vs. always-on tradeoffs, credential and token lifecycle, fan-out and fan-in patterns, fairness and quota enforcement across tenants, and the observability needed to debug at that volume.Make AI-assisted development a first-class platform layer: coding agents that review and ship code, automate CI, refactor at scale, and run as background workers across the codebase, together with the canonical scaffolding and guardrails that govern them.Build the systems that let every team; engineering, operations, and the business, run their own agents reliably and safely against the tools they already use, with the right credentials, scheduling, memory, and audit underneath.End-to-end ownership: architecture, implementation, rollout, observability, on-call, and iteration based on internal user feedback.Partner closely with security, infrastructure, and product teams to make agent deployments safe by default.You Might Be a Great Fit If You…Have 5+ years building backend systems in production, with deep proficiency in at least one of Python, TypeScript, Go, or Rust.Bring strong fundamentals in distributed systems: consistency, idempotency, retries, failure modes, queueing, scheduling.Have designed and operated APIs and services that other engineers depend on.Have a proven track record building shared infrastructure, internal platforms, or developer-facing services that real users adopted.Have strong intuition for developer experience, long-term maintainability, and where to draw abstraction boundaries.Are comfortable owning the full lifecycle: writing the design doc, shipping the MVP, hardening it, and driving adoption across the company.Have owned services with real uptime and operational responsibility, and are comfortable with observability stacks, incident response, and SLOs.Bring cloud-native experience: Kubernetes, infrastructure-as-code, OAuth/OIDC, secrets management.Ways you might stand out:Experience building or operating LLM infrastructure: gateways, inference systems, prompt routing, cost attribution, evaluation harnesses.Experience with agent frameworks, tool-use systems, or sandboxed code execution.Security instincts around prompt injection, supply-chain risk in agent ecosystems, and credential scoping for autonomous systems.Background in multi-tenant, regulated, or government deployments (HIPAA, SOC2).Open-source contributions to AI infrastructure, agent tooling, or developer platforms.Why Join UsCollaborate with industry veterans from Tesla, DeepMind, Databricks, and moreAccelerate your career with ownership based on impact, not tenureEarn competitive compensation + meaningful equity in a high-growth companyThrive in a culture built on speed, curiosity, and impactBenefitsCompetitive salary plus equityDaily lunchesCommuter benefits401(k)Medical, Dental and VisionUnlimited PTO

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About Basis Basis builds real agents that do real work in the real economy. Our agents operate for hours at a time, performing end-to-end work for some of the largest accounting firms in the world. We recently raised $100M at >$1B valuation and are racing to deploy the most advanced applied ML at production scale. Our investors include: Khosla Ventures (Keith Rabois & Vinod Khosla), Accel (Miles Clements), Google Ventures, Nat Friedman & Daniel Gross, Adam D'Angelo, Jeff Dean, Jack Altman, Noam Brown, Kyle Vogt, Amjad Masad, Clem Delangue and many other operators/technical leaders. "Basis is on the frontier of building production-grade, long-horizon agents. They've pushed the limits of what we thought our models could do on real-world, economically valuable, complex accounting tasks. They've been a great collaborator in helping us shape what the future of agents looks like." — Prashant Mital, Applied AI Lead, OpenAI The RoleEvery team building agents has access to the same models. Most of the attention goes to the product and its behaviors. The compounding work sits underneath: what the agent knows, how it reasons about its work, and how we measure whether it did the job well.At Basis, our agents autonomously perform real accounting work: reconciliations, tax preparation, audit procedures, financial analysis. Often, the quality of this work is not determined by which model we use. It is determined by the context those agents receive: the procedures that define what to do, the domain knowledge that defines how, and the evaluations that tell us whether it worked.To deliver frontier intelligence, we treat context like a product. The procedures, the skills, the system prompts. Each is owned, versioned, and held to a quality bar.Intelligence Architects build the coherent systems that construct our agents’ intelligence from disparate information. You hold the mental model of what each agent knows, how it reasons, and how we measure whether it's working. You write the procedures and skills. You define what "good" means at every layer and design how we measure it. Ultimately, you're orchestrating an intelligence whose ceiling rises with every model release, and whose capability compounds with every edit you make. What you'll be doingArchitecting and authoring our agents' context. You decide what context our agents need, how it's structured, and what each layer says. You write the procedures, skills, and system prompts they consume.Setting the standards. Ensuring consistency across our agents is your primary mission. You define how procedures should be written, what makes a skill document load-bearing, when context is ready to ship. Each product team operates to the standards you set.Signal to action. You watch the agents at work. When their output isn't good enough, you’ll define what's missing in the context and solve it. The measurement work is shared with product and eval teams; the writing work is yours.Shaping what comes next. Your perspective informs what we build next. You see across both the agents of today and the possibilities of tomorrow as the substrate improves. That view feeds into product decisions about which surfaces become possible and which existing ones graduate to higher capability. What you'll bringWriting as a craft. You treat language as load-bearing. You catch the sentence that will be misread before it ships. You have excellent taste.Reasoning about systems. You see how individual parts contribute to an overall system. When something breaks, your first question isn't what failed but how the system allowed it.Working from causes, not effects. You don't accept "it works" as evidence that you understand why. You want to know what’s caused the improvement, and which layer allowed the failure.Drawn to the frontier. The questions in this role don't have answers in any textbook. That's the part that excites you, not the part that worries you.Motivation by learning. You build accurate mental models of new fields fast. The accountants and engineers you work with should feel that you understand their work, not just talk about it.Comfort in ambiguity. There are often no right answers. What you write becomes binding and shapes thousands of agent decisions. You take that seriously without being paralyzed by it.Holding many things at once. You hold the user's problem, the agent's behavior, the procedure's language, and the measure of success in your head at once. You don't lose the thread. Who might be a fitThis role doesn't have a standard background. The people who do it well come from many places. What unifies the best candidates is a deep curiosity about the frontier of technology and what it enables.Engineers and PMs who've drifted from building systems to questioning whether the system is doing the right thing. You debug problems for users that aren't your job to fix.Philosophers who've spent years writing precise prose about systems no one else understood. The novelty doesn't intimidate you; it's the reason you do the work.Lawyers who write the kind of language other people have to follow. You've felt the difference between a phrase that holds and one that doesn't.Accountants who appreciate language and want to shape the future of your profession.Editors and writers who treat structured prose as engineering. You've seen instructions get misread at scale and you know why.Academics tired of producing work that nobody acts on. You want your writing to change something, and you want to be where the change is happening.Anyone who reads about what we're doing and recognizes the work as theirs. Benefits at BasisWe offer a competitive and thoughtful benefits package designed to support your physical, mental, and financial well-being:Health & Wellness: Premium Medical, Dental, and Vision coverage; Life Insurance; and 6 coaching & 6 therapy sessions through Spring Health.Time off: Unlimited PTO + 12 paid company holidays.In-Office Perks: Daily meal stipends, a fully stocked kitchen, and $300 toward your custom desk setup.Financial Benefits: Pre-tax commuter benefits and 401(k) retirement planTeam Culture: Monthly office activities and frequent optional team happy hours.Parental Leave

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🚀 About WRITERWRITER is where the world's leading enterprises orchestrate AI-powered work. Our vision is to expand human capacity through superintelligence. And we're proving it's possible – through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise-wide transformation. With WRITER's end-to-end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise-grade LLMs. Valued at $1.9B and backed by industry-leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI.Founded in 2020 with office hubs in San Francisco, New York City, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI.📐 About the roleEvery company grows differently, and at WRITER, our growth is directly tied to empowering our users to create better content, faster, and at an unprecedented scale. As a strategic solutions architect, you'll be at the forefront of this mission, working directly with our largest and most strategic prospects to identify, validate, and build innovative agentic AI solutions that unlock massive business value. This isn't just about selling a product; it's about deeply understanding complex enterprise needs and architecting a future where AI transforms how our customers operate. You'll be instrumental in shaping how the world's leading companies adopt and scale AI, making a tangible impact on both their success and WRITER’s continued leadership in the enterprise generative AI space.This is a full-time, hybrid role based out of our New York hub. You will report directly to the senior manager, solutions architecture.🦸🏻‍♀️ What you'll doDrive strategic technical discovery with Fortune 500 prospects and customers, translating complex business challenges into clear, impactful technical solutions for AI-powered workArchitect and design robust, scalable, and secure generative AI solutions for enterprise clients, leveraging WRITER's platform, APIs, and custom applications to solve critical business problemsLead the development and execution of compelling proofs of concept (PoCs) and demonstrations, building custom templates and integrating WRITER's capabilities to showcase transformative value and accelerate time-to-value for customersServe as a trusted technical advisor to C-suite executives, VPs of Engineering, and AI leaders, guiding their generative AI strategy and collaborating to define enterprise-level architecture roadmapsPartner closely with WRITER's product and engineering teams, providing critical feedback from customer engagements to influence our product roadmap and ensure our solutions meet evolving market needsChampion the adoption of WRITER's platform and APIs, educating prospects and partners on the art of the possible with generative AI and empowering them to build their own innovative solutions⭐️ What you need5+ years of experience in technical customer-facing roles such as solutions architect, enterprise architect, or sales engineering, ideally within a high-growth, B2B SaaS company serving Fortune 500 clientsDeep expertise in generative AI principles, large language models (LLMs), and prompt engineering best practices, with a passion for staying ahead of the curve in this rapidly evolving fieldExperience in Python, proficiency with APIs, and end-to-end orchestration, to augment AI workflows and integrate AI capabilities into complex enterprise systemsExceptional communication and presentation skills, able to simplify complex technical concepts into clear, compelling business language for diverse audiences, from technical teams to executive leadershipA highly versatile and tenured problem solver, capable of leading multi-stakeholder discovery sessions, uncovering root causes, and architecting innovative solutions to complex business and technical challengesA builder's mindset and a proven ability to connect deeply with customer needs, challenge the status quo with innovative AI solutions, and own the technical success and impact of client engagements 🍩 Benefits & perks (US Full-time employees)Generous PTO, plus company holidaysMedical, dental, and vision coverage for you and your familyPaid parental leave for all parents (12 weeks)Fertility and family planning supportEarly-detection cancer testing through GalleriFlexible spending account and dependent FSA optionsHealth savings account for eligible plans with company contributionAnnual work-life stipends for:Wellness stipend for gym, massage/chiropractor, personal training, etc.Learning and development stipendCompany-wide off-sites and team off-sitesCompetitive compensation, company stock options and 401kWRITER is an equal-opportunity employer and is committed to diversity. We don't make hiring or employment decisions based on race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other basis protected by applicable local, state or federal law. Under the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.By submitting your application on the application page, you acknowledge and agree to WRITER's Global Candidate Privacy Notice.

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Founding Research Scientist, Human SimulationTL;DR: Listen is building the human layer of AI: a preference model trained on millions of real human conversations. We're hiring a founding researcher to lead our simulation initiative, the model that lets AI systems predict what humans would think, want, and decide. Sequoia-backed, $100M raised, customers include Anthropic, Google, and Cursor. BackgroundAs AI gets better at building things, the bottleneck shifts to knowing what to build. We're the bridge between AI systems and what humans actually want. Today our customers are companies. Soon, AIs themselves will be our customers.Our platform runs AI-moderated video interviews at massive scale. We find the right people from a network of millions, our AI conducts open-ended conversations with thousands of them in parallel, and we surface what to build next. What used to take research teams weeks per study, we do in hours.Where it's going: every interview feeds a human preference model. We simulate human behavior at scale: how people react to new ideas, how they make decisions, how preferences shape markets, and how change ripples through society. We expose this as the Human API. An AI agent writes code, asks Listen whether users would actually want a feature, gets a grounded answer back, and iterates. Closed loop product development at AI speed. Every coding agent will eventually need this signal.Company highlightsSeries B with $100M raised from Sequoia, Conviction, Ribbit, AI Grant, and Pear VC.Selective team of <20 engineers including VC-backed founders, IOI medalists, and engineers from Jane Street and Tesla Autopilot.Customers include Anthropic, Cursor, Perplexity, Google, Microsoft, Robinhood, Nestlé, P&G, and Sweetgreen.Post-PMF growth: 20x year-over-year revenue.Huge market: clear path to $1M+ contracts at over 50% of the Fortune 2000.Research ChallengesModeling Humans. What does it take to actually understand a person? Which questions yield the most signal, how do we combine long-form interviews, demographics, and behavior into a useful model, and how do we predict a specific person's response to a question they've never been asked? Can we estimate how confident we are in a prediction?Multi-Agent Dynamics. People don't form opinions in isolation. They influence each other, deliberate, and shift in groups. Can we simulate cohorts of synthetic humans deliberating, reaching consensus, or splitting into camps?Generalization and Active Learning. With millions of interviews, how can we learn from patterns across people, contexts, and questions? When the model is uncertain, how do we go back to real humans to update the model?What we look forYou have a strong research track record. Published work in LLMs, post-training, RLHF, behavioral modeling, simulation, or adjacent fields. Or equivalent industrial impact at a frontier lab.You pick the problems. This is a founding research role. You'll define what's worth working on, scope research programs, and decide what success looks like.You're genuinely curious about humans. You want to understand what people actually want, how they decide, and why preferences shift.You make research real. You can train models, write evals, and collaborate with our research engineers to put the model into production.You communicate complex ideas in writing. This is how you share the roadmap and vision for this initiative.Life at Listen LabsTop of market compensation with meaningful equity.Comprehensive healthcare and dental, flexible time off, a culture that values balance and trust.Joining at an inflection point. PMF is real, the market ahead is enormous, the team is still small enough that your work directly shapes the company.

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About Tortus AIAt Tortus we work with clinicians and hospitals to provide clinically safe and reliable AI. We use speech-to-text and LLM models to infer information about a patient visit and provide verified documentation and actions — eliminating the documentation burden that costs clinicians hours every day.We've recorded over 1 million consultations. Clients include London Ambulance Service and a large proportion of UK GP surgeries. We are now entering our next phase: an AI doctor product and a ~£30M Series A raise.About the roleSenior research leadership at the intersection of product and science. You will define the AI research agenda, engage deeply with product on architectural choices, propose enhancements grounded in research, and technically guide the product engineering function in implementing those changes. You will also represent Tortus externally through publications and thought leadership — strengthening our credibility as a serious AI company operating in a high-stakes clinical domain.What you'll doDefine and lead the AI research agenda: clinical trust, safety, evidence-based AI, non-deterministic system designPartner with product on architectural decisions — bringing a research perspective to shape the approaches we choose, not just validate them after the factPropose enhancements to existing architectures and technically guide product engineers in implementing themPublish original research; contribute to the clinical AI academic communityRepresent Tortus at conferences and with clinical and regulatory stakeholdersContribute to our Class IIa medical device submissionSet the technical bar for AI hiring as the team scalesWhat we're looking forStrong research track record: publications, patents, or equivalent in uncertainty quantification, evidential deep learning, multi-agent systems, clinical NLP, or trust modellingTrack record working at the research/product interface — you can engage credibly with engineering teams and influence implementation without owning itExperience reasoning about non-deterministic systems and building defensively against model failureComfortable in a fast-moving, commercial healthtech environmentNice to haveExperience with regulated AI or medical device softwareFamiliarity with UK/EU AI regulatory landscape (MDR, UKCA, MHRA)Clinical domain knowledgeWhat does wild success look like?In 12–18 months, you have published at least two peer-reviewed papers that are cited in the clinical AI community and referenced in our Series A materials. The product team regularly seeks your input before committing to architectural decisions. You have built a research function that gives Tortus a credible voice in responsible clinical AI — and that directly shapes what we ship.Where does this role take you?This is a role with significant upside as Tortus scales. You could grow into Chief AI Officer as the research function expands, or shape a centre of excellence in clinical AI that attracts external researchers and clinical collaborators. The work you do here will influence how AI is used in medicine at scale.What we offer28 days holiday + bank holidaysLatest MacBook and the right equipmentOffice-first culture based at Holborn Town Hall — typically Tuesday, Wednesday, Thursday in office; Monday and Friday flexibleThe chance to genuinely transform healthcare

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Our Vision & Products🚀 EverAI — Building the Future of AI CompanionshipOne of the Top 15 Largest & Fastest-Growing AI Companies in the World50 Million Users in 2 years — Help Us Reach 100M first, 500M nextAt EverAI, we’re shaping what it means to connect with AI. With 50 million users and counting, we're not just building products — we're creating entirely new categories.Our flagship product is the world’s largest AI companionship platform, redefining relationships for millions. It is governed by our proprietary moderation system, EverGuard — an internal AI designed to ensure everything we build is safe, ethical, and human-first.And we’re only just getting started!Our TeamWe are an enthusiastic, passionate and hardworking team of ≈ 75 people. Our founding team has strong entrepreneurial experience building and scaling web products from 0 to IPO.Alexis Soulopoulos [CEO]• 10+ years in Tech Executive Leadership• Co-Founder Mad Paws Holdings (from 0 to IPO)• Forbes 30 under 30 + Deloitte TechFast50 ’22 & ‘23Michael Monin [Co-founder & CTO]• 10+ years as CTO / COO (web2/web3), 1+ year in AI/LLM• Serial-entrepreneur: MTK Digital (exited / 0->$20m revenue) and Zipchat (AI Chatbot for E-commerce brands)Thomas Lacroix [Co-founder & CMO]• 8+ years in Customer Acquisition & E-commerce Growth• Serial-entrepreneur: Curatible (sold to Blackstone) and MTK Digital (exited / 0->$20m revenue)Maruša Fasano [CFO/Legal]• 25+ years in Finance, Strategy, M&A• Ex-CFO/M&A @Curatible (exited to Blackstone)• Ex-President of the Board @SotremoSA (exited)• Co-founder/CFO @SoftOne (exited)Your RoleWe are looking for a Mid/Senior AI Cinematic Video Editor who is deeply embedded in generative video workflows and can independently craft high-quality, narrative-driven content from concept to final output.You are a sharp-eyed video editor comfortable operating at the intersection of creativity and emerging technology, building visually compelling longform videos using AI-first pipelines. You take ownership of the entire production stack — from prompting and generation to editing, compositing, graphic design work and final delivery. You thrive in a high-performance environment where your quick wits and passion for story-telling shine.Reporting to your Content Lead, this is what you'll be doing:Key ResponsibilitiesConceptualise scripts based on current production needs and centred around existing AI characters (our own IPs)Create narrative-driven, longform video content, including stylized and explicit NSFW visuals, with a strong focus on storytelling, atmosphere, and visual coherence throughoutOwn and manage end-to-end AI video production workflows, from ideation and prompting to generation, editing, and post-productionWork extensively with ComfyUI pipelines, building, customizing, and optimizing node-based workflows for image and video generationUtilize tools such as Stable Diffusion (AUTOMATIC1111), ComfyUI, Runway, Pika, and other AI video platforms to produce high-quality visual sequencesDevelop and maintain consistent character appearance, style, and scene continuity across longer narratives using advanced techniquesIntegrate motion graphic design and colour correction to deliver cohesive final outputsExperiment rapidly with new AI models, tools, and techniques, incorporating them into production workflows and sharing skills with the rest of the teamAlign with your Content Lead’s creative direction while maintaining a high degree of autonomy in execution and technical decision-makingContinuously refine workflows for efficiency, scalability, and output qualityYour QualificationsHard Skills4–6+ years of experience in video production, VFX, or digital art, with a strong portfolio (AI-driven video work is a must)Proven experience with ComfyUI (mandatory), including building and managing complex node-based workflowsStrong proficiency in Stable Diffusion / AUTOMATIC1111 and related tooling (Advanced prompting, working with trained LoRAs, refining, upscaling, inpainting etc.)Hands-on experience with the latest AI video tools and models (e.g., Kling, Seedance, Happy Horse, and similar emerging options)Solid understanding of AI image/video generation pipelines, including prompting, batching, consistency techniques, and post-processingExperience with traditional tools like Premiere Pro, After Effects or similar for final assembly and polishStrong grasp of visual storytelling, pacing, composition, and cinematic languageAbility to maintain character and scene consistency across sequences in AI-generated contentSoft Skills🗣 Strong communication & collaboration skills (fluent in English)🎯 High ownership — able to take ideas from concept to completion independently⚡️ Experimental mindset — comfortable navigating ambiguity and evolving tools⏱️ Fast iteration cycles — able to test, fail, refine, and improve quickly🧠 Curious and self-driven — constantly exploring new AI capabilities and workflows🧢 Open to feedback and continuous improvement🍭 #NSFW — Comfortable working with uncensored models and explicit adult contentBonus PointsExperience in AI-native storytelling or filmmakingBackground in 3D, VFX, technical art or as a colouristFamiliarity with training/customizing models (LoRAs, embeddings, fine-tuning)Prior experience in adult, dating, or adjacent creative industries a huge bonusWhy EverAI?📈 Exponential Growth: From 50M users in 2 years, to 100M next — and 500M beyond🚀 Track Record of Category-Creating Innovation: We consistently launch world-first AI applications — setting the pace, not following it🌍 Global Impact: Top-tier user growth, real-world adoption, and cultural relevance🧠 Proven Leadership: A senior team that’s launched, scaled, and exited & IPO’d multiple scale ups — now fully focused on reshaping AI companionship👥 Elite Remote Team: 100% remote and built to win — world-class talent from Tier 1 tech companies, with a culture of ownership, velocity, and radical creativity🛡️ Ethical Core: Our AI ecosystem is governed by EverGuard, our proprietary AI moderation technology, ensuring responsible development at scaleWhat We Offer✍️ Contract Type: We prefer B2B, but we’re flexible, what matters is long-term commitment and impact 📍 Work From Anywhere: Fully remote. Choose the environment where you do your best work 🏝️ Paid Time Off: 4 weeks (20 working days) of PTO per year to recharge and reset 👨‍👩‍👧‍👦 Annual Gathering: A yearly in-person meetup to connect, brainstorm, and celebrate wins together ❤️‍🩹 Health & Wellness Support: Monthly allowance of 100 USD for health insurance expenses + unlimited 1:1 sessions with psychologists and lifestyle experts through OpenUp (also available for up to three family members) 🏢 Co-Working Space Budget: Work from a co-working space up to twice per month (35 EUR / 40 USD per visit) to stay inspired and connected 📚 Learning Budget: Dedicated funds to support your professional growth: courses, books, conferences, events, or certifications 💻 Equipment: Company laptop provided + monitor budget up to 250 USD for your workspace setup ⚡ AI Tools Access: Premium access to ChatGPT, Cursor, Hugging Face, Claude Code, and any other tool needed to excel at your job, power your ideas and workflows🎯 Top Tier Talent Is Our MultiplierWe’re a fully remote group of A-players from Tier 1 tech, led by an exec team who’ve launched, scaled, and exited multiple companies. We move fast, and care deeply about what we build — and who we build it with.We’re looking for exceptional talent ready to ship & distribute world-first AI products at scale, fast, and co-create with us this category-defining business.If that’s you — reach out and apply!💡 External Referral ProgramKnow someone who could be a great fit for this role? You can refer them through the EverAI External Referral Program and earn a bonus of up to 2,500 USD if they’re hired. Submit a referral here.

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ABOUT FATHOM We created Fathom to eliminate the needless overhead of meetings. Our AI assistant captures, summarizes, and organizes the key moments of your calls, so you and your team can stay fully present without sacrificing context or clarity. From instant, searchable call summaries to seamless CRM updates and team-wide sharing, Fathom transforms meetings from a source of friction into a place for alignment and momentum. We’re a small company that creates magical experiences through the hard work of focused builders. We try to live our values - Care Deeply, Seek Leverage, Share Ownership, Sustain Urgency, and Be Tenacious - in everything we do, every day. We started Fathom to rid us all of the tyranny of note-taking, and people seem to really love what we've built so far: 🥇 #1 Most Used App of the Year on HubSpot for 2025🔥 #1 Rated on G2 with 4,500+ reviews and a perfect 5/5 rating🥇 #1 Product of the Day and #2 AI Product of the Year🚀 Most installed AI meeting assistant on both the Zoom and HubSpot marketplaces📈 We’re hitting revenue and usage records every week We think you’ll be pretty excited about Fathom too if you give it a try. Sign up today (it’s free)! ROLE OVERVIEWWe're hiring a Model Performance Engineer to own the speed, cost, and reliability of our model inference stack, and to build the fine-tuning infrastructure that makes the rest of the AI team faster.This is not a research role. You'll be optimizing real systems serving millions of meetings — choosing between quantization trade-offs, debugging speculative decoding, or figuring out why one GPU family's tail latency explodes at high concurrency while another stays stable.You'll own two things:1. Inference performance. You'll make our models faster and cheaper — speculative decoding, quantization, serving configuration, GPU selection, batching strategies, cold start mitigation, adapter swapping. Our traffic is extremely spiky (meetings end in 30-minute blocks), so you need to think about throughput curves. Our team greatly values offering a fast product.2. Fine-tuning pipelines. The AI team constantly fine-tunes models for new tasks — distilling large teacher models for classification, training adapters for domain-specific behavior, DPO for preference tuning. Right now each project reinvents the training loop. You'll build repeatable infrastructure so an AI Engineer can go more quickly from dataset to deployed model.HOW YOU’LL HELP US WINBenchmark FP8 quantization across GPU families, find that FP8 KV cache causes catastrophic repetition loops, identify static quantization as 6% faster than dynamic on certain hardware, and ship a production config that gets 1.3x speedup with <1% quality degradationEvaluate serving frameworks (vLLM vs SGLang) with speculative decoding — discover that ngram speculation degrades ASR quality while EAGLE3 draft models don't, and that torch.compile makes certain GPUs 7% slowerBuild a fine-tuning pipeline that takes a JSONL dataset and produces an optimized tune ready for serving, so a teammate can train a small classifier in an afternoon instead of a weekOptimize GPU spend — know which GPU families are best for batch workloads (stable under high concurrency) vs latency-sensitive paths (40% faster, but tail latency blows up under load), and when a 30% cost premium isn't worth itDebug production inference issues — trace a quality regression to a serving framework upgrade that changed the default attention backend, or find that audio format handling in the multimodal pipeline silently drops segmentsREQUIREMENTSHard Skills:Deep experience with LLM serving frameworks (vLLM, SGLang, TensorRT-LLM, or similar) — not just deploying them, but tuning them: attention backends, scheduling strategies, CUDA graph warmup, prefix cachingHands-on quantization experience — you've gone beyond "apply FP8 and hope." You understand weight vs activation quantization, per-channel vs per-tensor scaling, and when dynamic quantization introduces more overhead than it savesProduction fine-tuning experience — LoRA/QLoRA SFT, familiarity with training frameworks (ms-swift, Axolotl, torchtune, or similar), understanding of data formatting, learning rate schedules, and how to diagnose training failuresStrong Python. You'll write serving infrastructure, benchmarking harnesses, and training pipelines — not notebooksComfort with GPU profiling and performance analysis. You should be able to look at a benchmark result and know whether the bottleneck is compute, memory bandwidth, or scheduling overheadStrong signal:Cost modeling for GPU infrastructure — you've had to choose between GPU types and justify the tradeoffExperience with multimodal models (audio/vision encoders + LLM decoders)Experience with Modal, Ray Serve, or similar serverless GPU platformsUnderstanding of audio processing (codecs, chunking, sample rates)Experience building internal tooling that other engineers use — this role succeeds when the rest of the team ships fasterNot required:ML research background or publicationsPrompt engineering expertise (we have a team for that)Frontend or full-stack experienceMasters/PhD (though it's fine if you have one) WHAT'S IN IT FOR YOUThe opportunity to shape the foundational software services of a growing companyA role that balances innovation and incremental improvementA dynamic and collaborative engineering teamCompetitive compensation and benefitsA supportive environment that encourages innovation and personal growth WHY YOU SHOULD JOIN USOpportunity for impact. We’re established enough to ship instead of fighting fires and early enough that your work will have a real impact.Startup experience. You’ll work closely with our CEO, a 2X Founder/CEO with a background in computer science and product design.We embrace being fully remote. We schedule meetings sparingly and instead heavily use async comms (Slack, Notion, Loom)ABOUT THE INTERVIEWYou’ll meet the entire team. We think it’s important that you get to meet everyone you’ll be working with.No bullshit. Ask us anything you like. We’ve never understood why companies pretend they’re something that they’re not in the hiring process - you’re going to find out eventually so we’d rather you know who we are up front so we can both make sure this is a good fit for all involved.Quick turnaround time. We know you have lots of options so we move fast usually in less than a week from start to finish.HOW TO APPLYInclude a brief write-up or demo of inference optimization or model serving work you've done. We care about the reasoning behind your decisions — why you chose a specific quantization strategy, how you diagnosed a performance regression, what tradeoffs you navigated. A GitHub repo, blog post, or even a few paragraphs in your cover letter works.

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About the TeamOpenAI’s mission is to ensure that general-purpose artificial intelligence benefits all of humanity. We believe that achieving our goal requires real world deployment and iteratively updating based on what we learn.The Intelligence and Investigations team supports this by identifying and investigating misuses of our products – especially new types of abuse. This enables our partner teams to develop data-backed product policies and build scaled safety mitigations. Precisely understanding abuse allows us to safely enable users to build useful things with our products.About the RoleProtection Science Engineering is an interdisciplinary role mixing data science, machine learning, investigation, and policy/protocol development. As a Protection Scientist Engineer within Integrity and Investigations, you will be responsible for designing and building systems to proactively identify and enforce on abuse on OpenAI’s products. This includes ensuring we have robust abuse monitoring in place for new products, sustaining monitoring for existing products, and prototyping and incubating systems of defense against our highest risk harms. You will also respond to and investigate critical escalations, especially those that are not caught by our existing safety systems. This will require expert understanding of our products and data, and involves working cross-functionally with product, policy, and engineering teams.This role can be based in either our San Francisco, DC or NY office and includes participation in an on-call rotation that will involve resolving urgent escalations outside of normal work hours. Some investigations may involve sensitive content, including sexual, violent, or otherwise-disturbing material.In this role, you will:Scope and implement abuse monitoring requirements for new product launches.Improve processes to sustain monitoring operations for existing products, including developing approaches to automate monitoring subtasks.Prototype and mature into production systems of detection, review, and enforcement of abuse for major harms.Work with Product, Policy, Ops, and Investigative teams to understand key risks and how to address them, and with Engineering teams to ensure we have sufficient data and scaled tooling.You might thrive in this role if you:Have at least 4 years of experience doing technical analysis and detection, especially using SQL and Python.Have experience in trust and safety and/or have worked closely with policy, enforcement, and engineering teams. An investigative mindset is key.Have experience with basic data engineering, such as building core tables or writing data pipelines in production, and with machine learning principles and execution. Basic software development skills are a plus as this role writes productionised code.Have experience scaling and automating processes, especially with language models.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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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 RoleResearch Program Managers at Reflection are high-leverage leaders and operators who embed directly with research and engineering teams to accelerate the pace of frontier model development. They are not project trackers. They are force multipliers who bring clarity to ambiguity, drive decisions when the path forward is unclear, and ensure that the work happening across multiple teams connects into a coherent whole.This role is embedded directly within our Pre-training ML and Data teams, working alongside our Pre-training research leads at the core of how we build frontier models. You will be deeply involved in the research lifecycle, from data pipeline coordination and experiment planning to model architecture decisions and scaling strategies. Your job is to understand how our researchers work, identify the highest-leverage gaps, and build the programs, processes, and coordination structures that let them focus on pushing the frontier rather than navigating organizational complexity.You bring a first-responder mentality. When things go sideways, you don't wait to be asked. You jump in, assess the situation, cut through noise, align the people who need to be aligned, and drive resolution.What You'll DoEmbed within the pre-training ML and Data teams to deeply understand the technical landscape, build trust with researchers and technical leads, and identify where programs and process can have the most impact on research velocity.Drive end-to-end execution of complex, cross-team research initiatives that span data, model architecture, training runs, and evaluation, often without established playbooks.Coordinate the operational rhythm of pre-training research, including experiment prioritization, run scheduling, data readiness, and checkpoint handoffs to downstream teams.Equip research leadership to make decisions quickly by going deep on technical tradeoffs and presenting clear, actionable recommendations rather than status updates.Build lightweight processes that bring structure to unstructured research environments without adding friction. Create coordination mechanisms for cross-team handoffs, config management, and research milestones that replace ad hoc Slack threads with durable, visible systems.Act as the connective tissue between pre-training, mid-training, post-training, and infrastructure teams, ensuring that upstream decisions propagate cleanly and downstream teams are never surprised.About You7+ years of experience in technical program management, research operations, or ML engineering, with a track record of building programs from scratch in research or ML-heavy environments.Deep enough technically to engage with researchers on topics like model architecture, scaling laws, data processing pipelines, training dynamics, and optimizer behavior. You don't need to run the experiments yourself, but you need to follow the reasoning and spot risks early in order to discuss tradeoffs.Proven ability to embed within technical teams and earn trust through competence, reliability, and genuine curiosity about the work. Researchers want to work with you because you make them faster, not because the process says they have to.Resourceful and high-agency. You navigate ambiguity and shifting priorities without losing momentum. You figure out what needs to happen and you make it happen.Strong stakeholder management skills with the ability to influence senior technical staff through consistent delivery and well-informed judgment, not authority.Comfortable in high-stakes environments where decisions impact model quality, compute spend, and training timelines measured in weeks or months.Excited to build from zero to one. We are a small, fast-moving team and this role will help define how research program management works at Reflection.Motivated by enabling researchers and engineers to build the world's most capable open-weight AI systems.What 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 us:Articul8 was born from a simple belief: GenAI should work for the enterprise, not the other way around. Our platform combines domain-specific models, autonomous agentic reasoning through ModelMesh(TM), reliable model evaluation through LLM-IQ(TM), and multimodal understanding to serve regulated industries including energy, semiconductor, finance, aerospace, and supply chain. Trusted by Fortune 500 enterprises, we bring together research, engineering, product, and domain expertise to deliver AI that meets the accuracy, explainability, and auditability standards that high-stakes environments demand.Job Description:Articul8 AI is seeking a Principal Research Scientist to define how we build, evaluate, and scale domain-specific models as a durable source of competitive advantage. You will lead research across the full model development lifecycle: domain data strategy, continued pre-training, supervised fine-tuning, post-training, evaluation methodology, and the strategic decisions that determine where Articul8 can create and sustain model superiority in the market.Responsibilities:Set company-level technical direction for domain-specific model strategy — define how Articul8 builds, evaluates, scales, and sustains model superiority across continued pre-training, fine-tuning, post-training, and release quality standards, leveraging massively parallel agentic AI systems to compress strategic exploration cycles from months to daysArchitect the agentic model development paradigm for the organization — design the agent-orchestrated research infrastructure (experiment orchestration, data pipeline automation, continuous evaluation, competitive benchmarking) that enables every researcher at Articul8 to operate at a fundamentally higher level of depth, breadth, and velocity than would be possible aloneGo deep: push the frontier of domain-specific model science — lead research on model adaptation methodology, data curation strategies, post-training methods (preference optimization, reward modeling, reasoning improvement, alignment), and training dynamics, deploying fleets of agentic systems to run exhaustive ablation studies, mixture experiments, and failure analyses in parallelGo broad: shape model strategy across all of Articul8's domains and verticals — define how the company identifies, prioritizes, and enters new model domains based on technical feasibility, customer value, and strategic differentiation, using agent-driven competitive intelligence and market analysis to scan the landscape continuouslyDefine evaluation strategy as an agentic discipline — establish benchmark design, expert-grounded assessment, model failure analysis, and robustness standards, building always-on agentic evaluation harnesses that compare Articul8 models against leading open and closed alternatives and translate findings into concrete investment decisions in real timeLead cross-cutting research initiatives that multiply organizational capability — ensure advances in data perception, retrieval, post-training, and runtime orchestration strengthen the model layer, orchestrating parallel agent-driven research tracks across pillars so breakthroughs in one area compound across the platformInfluence platform-level decisions — shape model lifecycle management, portfolio strategy, release criteria, and integration architecture, ensuring the platform is designed for humans and agentic systems to co-evolve and amplify each otherMentor senior researchers and raise the ceiling on human potential — coach Staff and Senior researchers on designing agent-augmented research programs, raise the bar on technical judgment and experimental rigor, and shape hiring for researchers who are driven to redefine what's possibleMaintain hands-on research impact at the highest level — sustain a meaningful personal research contribution through technical work, publications, patents, and externally visible output, modeling what it means to be a world-class researcher who uses massively parallel agentic systems to achieve what was previously impossibleRequired Qualifications:Education: PhD or MSc in Computer Science, Machine Learning, NLP, or a related field.Experience: 10+ years in AI/ML research with an exceptional track record of impact — models or systems you built are in production and measurably changed outcomes. 4+ years developing LLM-based systems.Model lifecycle mastery: Deep hands-on experience across the full model development lifecycle — continued pretraining, supervised fine-tuning, post-training alignment, and production evaluation. You've made the hard calls about when a model is ready to ship and when it isn't.Evaluation rigor: You have designed evaluation methodology that goes beyond leaderboard metrics — domain-expert grounded assessment, systematic error analysis, robustness under distribution shift, and readiness criteria for high-stakes deployment.Training at scale: Direct experience training or adapting models on large GPU clusters using distributed frameworks (DeepSpeed, FSDP, Megatron-LM). You understand the interplay between data mixture, training compute, and model quality at a level that informs strategic decisions.Software engineering: Proficient in Python and PyTorch. You still write code, review code, and go deep when the problem demands it.Strategic leadership: You have shaped research direction at the organizational level — defining what bets to make, what to stop, and how to allocate research investment across competing priorities. People follow your direction because your judgment has been proven right.Preferred Qualifications:Experience building domain-specialized models that outperform general-purpose alternatives on specific, measurable tasks — not just fine-tuned checkpoints, but models with genuine domain understanding.Hands-on experience with post-training methods (RLHF, DPO, reward modeling, constitutional approaches) applied to real alignment problems, not just benchmark reproduction.Deep experience in data curation for model development — deduplication, mixture design, quality scoring — where your data decisions measurably changed model outcomes.Track record of designing evaluation frameworks for enterprise or regulated-industry use cases where a wrong answer has real consequences.Publication record at top-tier venues with evidence of sustained research leadership and influence on the field.Experience taking model research from prototype to production in a commercial setting where customers depend on the output.Domain expertise in one or more of: energy, semiconductor, finance, aerospace, or supply chain — you understand the data, the workflows, and why off-the-shelf models fail.Professional Attributes (Code42):Practice Humility: You lead with questions, not answers. You actively seek evidence that contradicts your strategy and revise publicly when warranted. You build an environment where senior researchers feel safe challenging your direction — because that's how the best decisions get made.Bias for Outcomes: You measure your impact by whether Articul8's models win in the market, not by the elegance of the research agenda. You make the hard calls about what to stop, what to double down on, and what to defer — and you own the results.Care Deeply: You treat the researchers you mentor as whole people, not output functions. You care about the quality of every model that ships under Articul8's name and intervene personally when standards are at risk. You build systems of feedback and recognition that make excellence visible.Dare to Do the Impossible & Embrace Scarcity: You define research bets that could change Articul8's competitive position for years. You don't let current scale limit the ambition of the model strategy. When resources are tight, you find the highest-leverage experiments and execute them with precision.Build a Better World: You ensure Articul8's model strategy serves not just business value but the industries and people who depend on these models for critical decisions. You hold the organization accountable for building AI that is trustworthy, auditable, and genuinely useful — because that's the only kind worth building.

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Why HarveyAt Harvey, we’re transforming how legal and professional services operate — not incrementally, but end-to-end. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.This is a rare chance to help build a generational company at a true inflection point. With 1000+ customers in 60+ countries, strong product-market fit, and world-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.Our team is sharp, motivated, and deeply committed to the mission. We move fast, operate with intensity, and take real ownership of the problems we tackle — from early thinking to long-term outcomes. We stay close to our customers — from leadership to engineers — and work together to solve real problems with urgency and care. If you thrive in ambiguity, push for excellence, and want to help shape the future of work alongside others who raise the bar, we invite you to build with us.At Harvey, the future of professional services is being written today — and we’re just getting started.Role OverviewAs a Backend Software Engineer on the Product Engineering team at Harvey, you will own and lead engineering projects across our various product lines. You will work closely with our AI, Legal, and GTM teams to build secure systems that deliver value to our customers. We are looking for individuals who have worked across the stack on incredible products across consumer, enterprise, and various industries and who are excited about building the future of application layer and genAI products.We use an in-person work model and offer relocation assistance to new employees.What You'll DoRetrieval over peta-byte scale documentsOrganizational-level interfaces to collaborate with AIManaging dedicated GPU capacity across 5+ regions1000-step planning agents that help take companies publicGovernment-level security for the world's most sensitive mattersEvaluating LLMs across a 10k+ leaf taxonomy of tasksInternet-scale data collection from over 50+ jurisdictionsWhat You Have10+ years (post-BS/MS) of backend-focused software engineering experience on product-centric teamsTrack record of building fast-growing SaaS products by leveraging PWA technologiesTrack record of shipping highly intuitive products, strong attention to detailExperience building backend platforms that can support multiple product linesStrong programming skills and general Computer Science knowledgeGrit - experience working at early-stage startups is a plusCompensation$231,000 - $340,000 USDDepending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices [here].#LI-SA1Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

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About Brain Co.Brain Co. is an applied AI startup co-founded by Jared Kushner and Elad Gil, and backed by leading Silicon Valley builders including Patrick Collison and Andrej Karpathy. We are building AI applications for the world’s most important institutions, delivering impact on real-world problems across governments, healthcare systems, and critical industries. Our progress so far:Automated construction permitting for a sovereign government → 80% faster, unlocking $375M+ in valueOptimized supply chains for a leading global energy company → 30% lower cost, 99% reliability, preventing $100M+ in lossesStreamlined hospital patient care across national health systems → 40% better outcomes, 80% less admin workCompany momentum:Raised a $55M Series A from leading investorsBuilt a team of 70+ AI experts from Tesla, Google DeepMind, NVIDIA, and Databricks At Brain Co., we focus on applying frontier AI to real institutional challenges, working alongside governments, healthcare systems, and critical industries to modernize how essential services operate. We are looking for leaders who want to help bring new technology into institutions that impact millions of people. About the Role:As a core backend engineer at Brain Co., you will build the shared technical capabilities that help us scale our AI products quickly, safely, and reliably. You will take ambiguous product and technical challenges, turn them into clear system designs, and ship robust platforms that support real-world AI applications for the world’s most important institutions. If you are driven by high-stakes engineering and thrive on delivering systems built for uncompromising reliability, this is the exact opportunity for you. What You’ll Work On:Design, build, and operate the platform backend services and data pipelines that power Brain Co.'s AI products. You will own the full lifecycle: from initial architecture and implementation to deployment and long-term maintenance.Build the critical systems that accelerate our AI product development. This includes designing scalable solutions for ML experiment tracking, artifact management, and automated training and evaluation pipelines.Engineer highly available, fault-tolerant systems with deep observability. Your architectures must be robust enough to meet the strict uptime and latency SLAs demanded by our enterprise and government clients.Design modular and scalable architectures, and clean APIs (REST, gRPC) with a long-term platform mindset. Continuously profile systems to ruthlessly optimize for latency, throughput, and cloud compute costs.Act as the bridge between engineering, product, and ML research. You will partner with these teams to build shared platform capabilities that remove bottlenecks and drastically reduce the time it takes to ship new AI products to our clients.You Might Be a Great Fit If You:You bring 3+ years of experience building and scaling production backend services or platforms, ideally using languages common in the AI/Data ecosystem (e.g., Python, Go, Rust, or C++).You don't just use frameworks; you understand what happens under the hood. You have a deep grasp of consistency, availability, distributed failure modes, and idempotency.You treat our internal ML and product teams as your primary customers. You have a strong intuition for building intuitive, well-documented, and highly maintainable APIs and shared platforms.You have a track record of owning services with real uptime expectations. You design for observability from day one and aren't afraid of on-call responsibilities for the systems you build.You excel at breaking down complex, open-ended problems into clear technical designs, moving from first principles to production-ready systems with both speed and rigor.You treat the platform as your own. You care about the end-to-end lifecycle of your systems.Bonus Points For:You have integrated or managed standard ML experiment tracking, artifact management, and model registries (e.g., Weights & Biases, MLflow, ClearML) or orchestrators (e.g., Ray, Flyte, Kubeflow).You have designed and operated high-throughput data pipelines and resilient asynchronous workflows using durable execution engines (e.g., Temporal), streaming platforms (e.g., Kafka), or distributed compute frameworks (e.g., Spark, Flink).You have navigated strict compliance frameworks (e.g., SOC2, FedRAMP) or built systems specifically for highly regulated, air-gapped, or on-premise environments.Why Join Us:Work alongside senior engineers from Tesla, DeepMind, Databricks, and other top engineering orgs.Ship fast, learn constantly, and see your work protect production systems used by millions.Earn competitive compensation and meaningful equity in a high-growth company.BenefitsCompetitive salary plus equityDaily lunchesCommuter benefits401(k)Medical, Dental, and VisionUnlimited PTO

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Why HarveyAt Harvey, we’re transforming how legal and professional services operate — not incrementally, but end-to-end. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.This is a rare chance to help build a generational company at a true inflection point. With 1000+ customers in 60+ countries, strong product-market fit, and world-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.Our team is sharp, motivated, and deeply committed to the mission. We move fast, operate with intensity, and take real ownership of the problems we tackle — from early thinking to long-term outcomes. We stay close to our customers — from leadership to engineers — and work together to solve real problems with urgency and care. If you thrive in ambiguity, push for excellence, and want to help shape the future of work alongside others who raise the bar, we invite you to build with us.At Harvey, the future of professional services is being written today — and we’re just getting started.Role OverviewAs a Backend Software Engineer on the Product Engineering team at Harvey, you will own and lead engineering projects across our various product lines. You will work closely with our AI, Legal, and GTM teams to build secure systems that deliver value to our customers. We are looking for individuals who have worked across the stack on incredible products across consumer, enterprise, and various industries and who are excited about building the future of application layer and genAI products.We use an in-person work model and offer relocation assistance to new employees.What You'll DoRetrieval over peta-byte scale documentsOrganizational-level interfaces to collaborate with AIManaging dedicated GPU capacity across 5+ regions1000-step planning agents that help take companies publicGovernment-level security for the world's most sensitive mattersEvaluating LLMs across a 10k+ leaf taxonomy of tasksInternet-scale data collection from over 50+ jurisdictionsWhat You Have5+ years (post-BS/MS) of backend-focused software engineering experience on product-centric teamsTrack record of building fast-growing SaaS products by leveraging PWA technologiesTrack record of shipping highly intuitive products, strong attention to detailExperience building backend platforms that can support multiple product linesStrong programming skills and general Computer Science knowledgeGrit - experience working at early-stage startups is a plusCompensation$193,400 - $290,000 USDDepending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices [here].#LI-SA1Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

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About The TeamThe Alignment Training team studies how frontier models acquire durable behavioral tendencies across the training stack. We work on identifying which behaviors can be shaped through pre-training, mid-training, and post-training; building the data, objectives, and evaluations needed to influence them; and determining whether the resulting behavior reflects a general learned tendency or a narrow artifact of the training distribution.Our work spans synthetic data, pre-training, mid-training, post-training, model behavior, and evaluation. We study how models learn to interpret intent, follow instructions, reason through tasks, express uncertainty, act honestly, and remain reliable under new conditions. The goal is to make desirable tendencies emerge early, strengthen throughout training, and appear robustly in deployed systems. About The RoleWe’re looking for a senior researcher with exceptional technical depth in large-scale model training, synthetic data, or evaluation who is excited to study how training choices shape aligned behavior in frontier models.You will help shape the research agenda for alignment training: defining the behaviors we want models to learn, designing data and training interventions to teach them, and building the evaluation loops needed to tell whether those behaviors are broad, robust, and durable. The strongest candidates will be able to move from an ambiguous behavioral question to a concrete experimental program: formulate the hypothesis, design the intervention, build the pipeline, run the experiment, and decide whether the result is real.This role is especially well suited for someone who wants to work close to the core model training loop, where choices about data, objectives, and evaluation directly shape how aligned deployed systems are. In this role, you'll:Develop synthetic data methods that teach models higher-level behavioral tendencies, such as understanding user intent, following instructions reliably, reasoning clearly, being honest, and acting consistently with intended goals and constraints.Study how pre-training, mid-training, and post-training each shape downstream model behavior, and which interventions are best applied at which stage.Build evaluation loops that connect model behavior back to training data and training objectives, so the team can iterate faster and with clearer signal.Design reusable data generation and filtering pipelines that improve the quality, diversity, and robustness of training data.Create experiments that distinguish durable learned behavior from benchmark gains, distribution-specific effects, or evaluation artifacts.Collaborate across pre-training, post-training, alignment, and product-facing teams to translate research insights into better model behavior.Help define the research agenda for alignment training: which behaviors should remain invariant across settings, which should adapt, and how to measure whether models have learned an underlying principle rather than a surface pattern.You might thrive in this role if you: Have a strong record of technically excellent work in large-scale ML, especially in pre-training, post-training, synthetic data, model evaluation, or training infrastructure.Are comfortable designing experiments where the signal is subtle, noisy, or indirect.Can move between research taste and engineering execution: forming hypotheses, building pipelines, running experiments, analyzing results, and turning findings into the next iteration.Have unusually good judgment about which research questions are worth pursuing and which signals are strong enough to trust.Care about making models more useful, honest, steerable, and reliable for real users.Are excited by alignment problems, even if you have not worked in alignment before.Communicate clearly across research, engineering, and product contexts.Prefer practical, evidence-driven work grounded in experiments.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.