Research Program Manager

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 moving complex, ambiguous technical initiatives forward across multiple teams and stakeholders.

- Can distinguish what is known from what is uncertain, ask incisive technical questions, and help teams make sound decisions with incomplete information.

- Know how to introduce structure without creating unnecessary processes, and care about making researchers more effective.

- Bring a high bar for quality and can recognize when evidence, evaluation results, or execution plans need closer scrutiny.

- Communicate clearly across technical and nontechnical audiences, including researchers, engineers, and organizational leaders.

- Take ownership without waiting for perfect conditions, adapt quickly as priorities change, and follow difficult problems through to resolution.

- Care deeply about advancing AI research responsibly and helping ensure increasingly capable systems benefit humanity.