Research Intern in San Francisco

About the Team OpenAI develops models that can reason through complex problems and hardware designed for the demands of advanced AI. AI for Chips connects these efforts: applying increasingly capable AI systems to the work of semiconductor engineering. Our goal is to help engineers develop better chips and shorten design cycles. This work brings research, model training, and hardware expertise together to build tools that engineers can use on real designs, with correctness and measurable performance at the center. About the Role We’re hiring a Research Engineer to help OpenAI models solve chip-design problems through reinforcement learning, tool use, and evaluation. You’ll own experiments from the initial idea through implementation and analysis. That means building environments and evaluations, running training, investigating failures, and using the results to decide what to try next. You’ll also build the software needed to make those experiments reliable and reproducible. We value strong coding fundamentals, careful experimental judgment, and the ability to make progress independently. Prior chip-design experience is helpful, but you can learn the domain alongside the team’s hardware specialists. In this role, you will: Build RL environments and evaluations for tasks such as RTL generation, design verification, and physical design optimization. Develop and test approaches that help models use chip-design tools and improve power, performance, and area while preserving correctness. Design experiments, establish baselines, and measure whether improvements hold up on new tasks and designs. Investigate failures across model behavior, rewards, evaluation tools, and experiment infrastructure. Improve iteration speed through better tooling, faster evaluations, and proxy rewards that reflect the outcomes we care about. Turn successful experiments into reusable research code and training workflows, working closely with researchers and engineers. You might thrive in this ro

About the Team OpenAI’s Governance team helps shape how the company responsibly develops and deploys increasingly capable AI. We bring together technical evidence, policy, and operational perspectives to help the company address emerging risks, resolve difficult questions, and make well-supported decisions. Our work includes shaping and improving governance practices, supporting effective oversight, developing clear assessments and recommendations, and ensuring that decisions lead to action. We work closely with research, safety, security, legal, and product teams to identify gaps, reconcile different views, and improve our approach as capabilities and circumstances change. About the Role We are looking for a curious, high-agency Research Program Manager who can reason from first principles, make sense of incomplete or conflicting information, and move difficult work forward. You will help shape the substance of governance reviews, connect ideas and evidence across teams, and develop recommendations that are both well-founded and practical. The work requires someone who can use established approaches where they fit, recognize when circumstances call for a different approach, and update their thinking as new evidence emerges. You should bring sound judgment, a willingness to experiment and learn, and the program-management discipline to turn good analysis into action. Depending on your experience and the team’s needs, your work may focus on safety advisory and board-level oversight, deployment governance, or standards and strategic partner commitments. In this role, you will: Bring together technical, policy, and operational inputs to develop coherent assessments, recommendations, and decision materials for governance bodies and senior leaders. Work through emerging or ambiguous questions, test assumptions, identify gaps or conflicting evidence, and help determine what additional analysis or decisions are needed. Engage critically with research, evaluations, safeguar

About the Team OpenAI’s Research Program Management team partners with researchers and engineers to advance the development of increasingly capable, safe, and beneficial AI systems. We work alongside teams developing our core models, helping turn ambitious research goals into coordinated execution across model training, alignment and safety, and research infrastructure. The team also regularly collaborates with our closest cross-functional partners such as Security, Applied product and engineering, Strategy, and Scaling. About the Role As a Research Program Manager, you will embed with research teams and help drive some of the most technically complex and consequential work behind OpenAI’s model development. Depending on your focus, your work may span training, reasoning, evaluations, compute, research infrastructure, safety, model launch readiness, and governance. You will translate evolving research priorities into actionable programs, help teams navigate technical and operational tradeoffs, and keep important work moving as new issues emerge. This is a hands-on technical role: you will engage directly with research workflows, experimental results, technical systems, and engineering constraints; not simply coordinate from the sidelines. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. You might thrive in this role if you: Have 5+ years of experience in research program management, technical program management, or related roles in fast-moving environments. Can engage substantively with researchers and engineers on topics such as model training, experimental design, model safety, evaluation methods, data workflows, compute infrastructure, or distributed systems. Are comfortable working directly with technical tools, research data, experimental results, or operational workflows to understand problems and develop practical solutions. Have a strong track record of movi

About the Team The Health team, within OpenAI’s broader Personal AGI organization, has a mission to ensure AGI improves health for all humanity. Improving human health will be one of the defining impacts of AGI. Hundreds of millions of people already turn to ChatGPT for questions about their health and millions of clinicians use it weekly to support care delivery. Increasingly capable models create an opportunity to make high-quality medical intelligence more accessible across patients and clinicians—raising the floor of human health—and accelerate the new capabilities and scientific advances that raise the ceiling of human health. Our job is to make those benefits real. We work across the full model stack—pretraining, midtraining, reinforcement learning, post-training, evaluations, harnessing, and deployment—and connect that research to the patients, clinicians, and real-world outcomes we aim to improve. About the Role We’re looking for an exceptional, hands-on researcher who wants to build frontier health capabilities and turn them into impact at scale. This is a role for someone who can take an important, underdefined problem from 0→1: identify the right bet, build what’s needed to test it, and drive it all the way to a measurable improvement in the models and products we actually ship. We’re especially excited about two kinds of people: researchers with the technical depth to move the frontier in pretraining, reinforcement learning (RL) / post-training, or evals; and researchers with real depth in developing frontier biomedical AI capabilities. Prior experience in healthcare is helpful but not required. Research excellence, velocity, ownership, and alignment with the mission are most important to us. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own a high-leverage research direction end to end—from deciding which problem matters and h

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE This role sits at the frontier of our research agenda. You will pursue open problems at the intersection of post-training methodology and performant inference, and then collaborate with research engineering to translate findings into production systems. A meaningful portion of your time will be dedicated to research that deepens our understanding of how models learn, alignment, and architectural efficiency — questions that may not have immediate product application. The remainder will be directed toward research that solves concrete problems for Baseten's platform and customers, who are the fastest growing AI companies in the world like Cursor, Lovable, and Notion. We are looking for someone with sharp research taste and genuine creative instinct for problem selection. Someone who can identify questions that matter, design clean experiments to answer them, and push the state of the art. The environment here is not theoretical, but rather research that can be validated with eager customers who are serving billions of tokens a second. RECENT RESEARCH Towards infinite context windows: neural KV cache compaction Dense, on-policy or both? Repeated kv cache for long-running agents Distillation without the dark – replicating black-box on-policy distillation on Baseten RESPONSIBILITIES Define and pursue a research agenda spanning both foundational and applied work, with the applied component connected to Baseten's pla

About the Team The Proactivity Research team, within OpenAI’s broader Personal AGI team, is focused on making our models in ChatGPT and future potential products proactive in ways that are truly useful. We're laying the technical foundations for AI that can anticipate what users need in real time, adapt as their goals and preferences shift, and build a deeper, evolving understanding of the person it's helping. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models’ personalization and agentic capabilities. Our team works on reinforcement learning, dataset creation, evaluations, and other post-training methods. We partner closely with research and product teams across the company to realize the vision of a highly personalized, collaborative, and proactive assistant. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own and pursue a research agenda to improve the proactivity and ability of our models to further user goals. Build robust evaluations for tracking modeling improvements. Design, implement, test, and debug code across our research stack. Collaborate closely with the other research and product teams to influence the shape of technical solutions in the product You might thrive in this role if you: Have a deep understanding of machine learning and machine learning applications. Have a working knowledge of LLM post-training and evaluation approaches Are passionate about, or have experience thinking about, personalization and enabling users to achieve their goals Are comfortable diving into a large ML codebase to debug. Thrive in a dynamic and technically complex environment. About OpenAI

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of Agent Post-Training, Computer Use, you will teach models to operate computers. You will help train models that can navigate browsers and desktops, use tools and applications, reason through complex workflows, collaborate with users and other agents, and complete long-horizon tasks with reliability and judgment. This work sits at the intersection of frontier model training, product behavior, evaluation, and systems engineering, and will directly shape the computer-use capabilities shipped in OpenAI’s next generation of agents. Currently, our models are the best in the world at this behavior! You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you might Design and run experiments th

About the Team The Personal AGI team is responsible for training and improving pre-trained models to be deployed into ChatGPT, the API, and potential future products. The team partners closely with research and product teams across the company, and conducts research as a final step to prepare for real world deployment to millions of users, ensuring that our models are safe, efficient, and reliable. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models. Our team works in research areas combining reinforcement learning and products. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own and pursue a research agenda to improve model capability and performance. Collaborate closely with the other research and product teams, allowing customers to optimize their own models. Build robust evaluations for tracking modeling improvements. Design, implement, test, and debug code across our research stack. You might thrive in this role if you: Have a deep understanding of machine learning and machine learning applications. Have a working knowledge of relevant models, and building evaluations for model capability improvement. Are comfortable diving into a large ML codebase to debug. Thrive in a dynamic and technically complex environment. About OpenAI OpenAI 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

About the Team The RL and Reasoning team drives the core reasoning paradigm and has created groundbreaking innovations such as o1 and o3. They focus on pushing the boundaries of reinforcement learning research, building next-generation generative models, and deploying them at scale. About the Role As a Research Engineer/Research Scientist at OpenAI, you will advance the frontier of AI alignment and capabilities through cutting-edge RL methods. Your work will sit at the heart of training intelligent, aligned, and general-purpose agents, including the systems that power various models. We’re looking for people who have a background in reinforcement learning research, are able to iterate quickly, and are proficient at coding. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. You might thrive in this role if: You love being on the cutting edge of RL and language model research. You’re a self-starter who takes initiative and ownership of ideas, driving them to completion. You value principled approaches, simple experiments in tightly-controlled settings, and reaching trustworthy conclusions which stand the test of time. You thrive in a fast-paced, dynamic, and technically complex environment where rapid iteration is key. You’re comfortable diving into a large ML codebase to debug and improve it. You have a deep understanding of machine learning and machine learning applications. About OpenAI OpenAI 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 ful

About the Team The Human Data team at OpenAI is responsible for identifying and mitigating risks in advanced AI systems by designing evaluations, surfacing vulnerabilities, and collaborating closely with researchers to strengthen model reliability and public trust. About the Role As a Research Program Manager, you will lead initiatives that test the safety and robustness of OpenAI’s models through creative experimentation and structured evaluation. You’ll coordinate efforts across research and engineering teams to transform ambiguous risks into concrete research programs and influence future model development and deployment. We’re looking for people who are technically savvy, comfortable with ambiguity, and excited about shaping the future of safe AI. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Lead programs that explore unexpected model behaviors and identify failure modes. Translate vague or emergent risk signals into clear priorities and actionable research plans. Design and run creative evaluations, experiments, and red-teaming campaigns. Collaborate with research, product, and deployment teams to integrate findings into model training and deployment cycles. Develop repeatable systems for tracking model performance and understanding emerging behavior patterns. You might thrive in this role if you: Have strong experience in technical program management, with excellent organizational and communication skills. Are familiar with large language models, prompt engineering, or model evaluation techniques. Are comfortable managing fast-paced, high-uncertainty projects and shaping them from the ground up. Are creative and resourceful in devising new methods for testing model behavior and performance. Can effectively coordinate across technical and non-technical stakeholders to drive alignment and execution. About OpenAI OpenAI is an AI resear

🚀 About WRITER WRITER 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 role AI research at WRITER isn't just about publishing papers — it's about building the scientific foundation that powers some of the most ambitious enterprise AI deployments in the world. As an AI research scientist, you'll be at the center of that work. You'll drive a high-impact research agenda focused on large language models, agentic reasoning, and the system-level capabilities that make AI genuinely useful at enterprise scale. This is a rare opportunity to do research that matters twice over — advancing the field and shipping directly into products used by hundreds of thousands of people every day. We're at an inflection point. Enterprises are moving from experimenting with AI to deeply embedding it across their operations, and WRITER's models are the engine making that possible. The work you do here — on post-training, planning, multi-step reasoning, and agentic workflows — will directly shape how the next generation of enterprise AI behaves, performs, and scales. You

About the Team OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make rigorous, evidence-based talent decisions and ensures that the systems supporting those decisions are valid, reliable, fair, and accountable. About the Role As a People Data Scientist focused on AI fairness and bias testing, you will help establish how OpenAI evaluates AI-assisted People systems and high-impact talent processes. You will design and conduct rigorous assessments to identify, measure, and mitigate potential bias across the lifecycle of models, agents, decision-support tools, and automated workflows. Your work will span the entire employee life-cycle, such as hiring, performance, promotion, employee development, workforce planning, etc. You will evaluate both technical systems and the broader human-AI decision processes in which they operate, examining not only model performance but also data quality, measurement validity, differential outcomes, human oversight, and unintended consequences. We’re looking for an experienced data scientist or applied researcher who can translate complex fairness questions into defensible evaluation strategies, scalable testing infrastructure, and clear recommendations for technical teams and senior leaders. This role is preferred to be based in San Francisco, CA. In this role, you will: Define and lead fairness and bias-testing strategies for AI-assisted People processes, models, agents, and decision-support systems from development through deployment and ongoing monitoring. Design rigorous algorithmic audits and validation studies, including adverse-impact analysis, subgroup and intersectional evaluation, error-rate analysis, calibration, measurement invariance, reliability, criterion-related validity, and sensitivity testing. Identify the appropriate fairness criteria for each use case, evaluate tradeoffs among competing definitions

About the Team OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make better, evidence-based talent decisions. About the Role As a People Research Scientist, you will bring deep expertise in research design, measurement, experimentation, and applied data science to OpenAI’s most important People programs. You will design studies, evaluate people processes, and help leaders better empower employees, strengthen organizational systems, and deliver exceptional employee experiences. This is a high-ownership individual contributor role combining hands-on research, methodological leadership, and scalable people science capabilities. We’re looking for an experienced researcher who can turn ambiguous People questions into rigorous designs, validated insights, and actionable recommendations. This role is based in San Francisco, CA or Mountain View, CA, with occasional travel to our San Francisco office. What You’ll Do: Design rigorous research and evaluation strategies for recruiting, organizational health, manager effectiveness, employee experience, and talent outcomes. Apply advanced statistical modeling, machine learning, and research methods to inform program design, evaluate effectiveness, and quantify business impact. Partner with People Operations, data engineering, and people systems teams to define data requirements, improve data quality, establish documentation standards, and ensure research datasets are governed, reproducible, and privacy-preserving. Build scalable people science infrastructure, including self-service agentic tools, automated validation workflows, reusable research datasets and analytical pipelines. Develop research playbooks that establish rigorous standards for study design, measurement, validation, and documentation, enabling high-quality, repeatable, and scalable research across the organization. Communicate findings through c

🚀 About WRITER WRITER 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 role AI research at WRITER isn't just about publishing papers — it's about building the scientific foundation that powers some of the most ambitious enterprise AI deployments in the world. As an AI research scientist, you'll be at the center of that work. You'll drive a high-impact research agenda focused on large language models, agentic reasoning, and the system-level capabilities that make AI genuinely useful at enterprise scale. This is a rare opportunity to do research that matters twice over — advancing the field and shipping directly into products used by hundreds of thousands of people every day. We're at an inflection point. Enterprises are moving from experimenting with AI to deeply embedding it across their operations, and WRITER's models are the engine making that possible. The work you do here — on post-training, planning, multi-step reasoning, and agentic workflows — will directly shape how the next generation of enterprise AI behaves, performs, and scales. You

About the role We are building a higher education researcher motion that helps funded labs adopt OpenAI across core research workflows. This role will develop relationships with principal investigators, researchers, research software engineers, research computing teams, and university technology leaders, then translate high-value use cases into sustained Pro, Codex, API, and ChatGPT Edu usage. This is a hybrid business development and program management role. You will identify and qualify priority labs, design phased access and fellows programs, run pilots, remove technical and institutional blockers, and create repeatable paths from individual researchers to lab- and institution-level adoption. Additionally, this role will launch and manage a community of researchers with events, communications and support. In this role, you will: Build a pipeline of funded labs, research centers, and technical champions at priority R1 universities; qualify opportunities based on workflow value, funding path, influence, and expansion potential. Run discovery with researchers and university stakeholders to understand workflows, data and security needs, procurement constraints, and success criteria. Design and operate phased access and fellows programs, including eligibility, selection, offer mechanics, onboarding, office hours, community programming, and pilot goals. Translate research workflows into effective use of Pro, Codex, API, and ChatGPT Edu in partnership with Solutions, Product, and Education account teams. Manage pilots end to end, remove trust, funding, and technical blockers, and drive measurable activation, retention, and expansion. Turn early usage into product feedback, workflow documentation, peer proof, case studies, and repeatable enablement. Build clear handoffs and expansion paths from researcher to lab to institution; track fellow selection, activation, retained usage, workflow proof, conversion, and expansion. You might thrive in this role if you: Relevant exp