Model Policy

Location

San Francisco

Full time

Hybrid

Safety Systems

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

Our Safety Systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency.

Within Safety Systems, the Model Policy team aligns model behavior with desired human values and norms. We co-design policy with models and for models by driving rapid policy taxonomy iteration based on data and defining evaluation criteria for foundational models’ ability to reason about safety.

Frontier AI systems are expanding what people can do across domains, creating both opportunities and safety questions. You will help define how OpenAI’s models should behave in high-risk or high-ambiguity contexts, such as agentic systems, multimodal systems, user safety, privacy, and other emerging risk domains.

This is an ideal role for someone who can move across unfamiliar topics, reason from first principles, and turn ambiguity into practical model behavior. You will work with research, engineering, product, preparedness, and operations teams to build policies that are technically grounded, measurable, and responsive to real-world risk.

You might thrive in this role if you:

• Have strong judgment about how advanced AI systems may affect real-world risk, especially in ambiguous, fast-moving, or high-impact areas.

• Have experience building or applying policies, taxonomies, harm models, threat models, or risk frameworks for complex technical, social, or adversarial systems.

• Can move across domains without needing to be the deepest subject-matter expert in every area, while knowing when to seek expert input.

• Can turn fuzzy questions into structured policy frameworks, evaluation criteria, operational guidance, and enforceable model behavior.

• Are comfortable using empirical evidence, including evaluations, red-teaming results, deployment observations, and model failure modes, to inform policy decisions.

• Think in systems across policy, data, graders, classifiers, training, deployment safeguards, measurement, monitoring, and escalation workflows.

• Have technical judgment about what model behavior can realistically be trained, measured, evaluated, and enforced at scale.

• Work well across research, engineering, product, policy, domain…