OpenAI's Strategic Deployment team aims to make frontier models more capable, reliable, and aligned to transform high-impact domains, by deploying models in real-world settings, gathering insights, and using these learnings to shape frontier model development and build the science and engineering of impactful frontier model deployment.
Requirements
- Have a research background in ML, deep learning, or related areas (e.g., RL, evaluation science, robustness).
- Have strong engineering skills and are comfortable diving into a large ML codebase to debug and improve it.
- Are interested in foundational research informed by real-world deploymentnot just paper benchmarks.
- Enjoy building tools, datasets, or infrastructure to elicit deeper insight.
Responsibilities
- Conduct research on real-world-informed model generalization, robustness, and steerability.
- Design challenging evaluations that capture real-world utility and reveal frontier model capability gaps.
- Use real deployments as a way to generate learnings to guide OpenAIs frontier model program.
- Collaborate with other researchers, infrastructure teams, and in some cases, domain experts and/or partners in strategic industries to surface major impact opportunities.
- Tackle fundamental research questions and AI engineering challenges grounded in real-world deployment.
- Build the science and engineering of reliable customization of frontier models.
- Develop understanding and evaluations that fuel strategically-important AI deployments and guide OpenAIs frontier model program.
Other
- This role is based in San Francisco, CA.
- We follow a hybrid model (3 days/week in-office) and offer relocation support.
- Are excited by ambiguous, open-ended problem spaces with high impact and stakes.
- Want to shape how frontier models evolve toward AGI.
- We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
