Model Policy Manager

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.
• Can 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.
• 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.