Model Policy

Compensation

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range is $207K – $295K, with offers of equity included. Total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and various benefits.

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.

About the Team

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.

About the Role

This role is ideal for someone who can move across unfamiliar topics, reason from first principles, and turn ambiguity into practical model behavior. 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.

Responsibilities

• Design and maintain model policies across safety-relevant domains, including dual-use, agentic, and emerging frontier-risk areas.
• Translate risk and harm models into clear behavioral specifications, evaluation criteria, grading guidance, and system-level safeguards.
• Define practical boundaries between beneficial uses of AI and assistance that could materially enable harm, exploitation, misuse, or unsafe outcomes.
• Build policy artifacts that support model training, evaluation, and deployment.
• Partner with safety researchers, engineers, product teams, and other stakeholders to operationalize policy into scalable model behavior and measurable safeguards.
• Use red-teaming results, deployment data, model failures, over-refusals, under-refusals, and ambiguous edge cases to improve policy and evaluation quality over time.
• Identify emerging capability areas where frontier AI systems could create new safety challenges or lower barriers to harm.
• Study real-world deployments to identify where model behavior succeeds, fails, or drifts from the intended safety posture.
• Combine longer-horizon safety research with hands-on launch and deployment work.
• Contribute to system cards, safety reports, policy documentation, launch reviews, and external communications on OpenAI's approach to model safety and risk mitigation.
• Design and run human data campaigns, including gold set construction, labeling guidance, calibration, adjudication, and eval coverage analysis, to ensure policies can be reliably measured and improved.

Requirements

• Strong judgment about how advanced AI systems may affect real-world risk, especially in ambiguous, fast-moving, or high-impact areas.
• Experience building or applying policies, taxonomies, harm models, threat models, or risk frameworks for complex technical, social, or adversarial systems.
• Ability to move across domains without needing to be the deepest subject-matter expert in every area, while knowing when to seek expert input.
• Ability to turn fuzzy questions into structured policy frameworks, evaluation criteria, operational guidance, and enforceable model behavior.
• 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.
• Technical judgment about what model behavior can realistically be trained, measured, evaluated, and enforced at scale.
• Work well across research, engineering, product, policy, domain experts, and operational teams.
• Write clearly about complex tradeoffs where safety, user value, and implementation constraints all matter.
• Take a pragmatic approach to safety, focused on reducing real-world risk while preserving legitimate, beneficial, and socially valuable uses of AI.
• Enjoy fast-paced, collaborative research environments where priorities shift as models, evidence, and risks change.
• Stay grounded in implementation details, empirical results, and what can actually be trained or measured.