Openai

Overview

In this role, 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.

About Openai

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.

Requirements & Eligibility

• Has strong judgment about how advanced AI systems may affect real-world risk, especially in ambiguous, fast-moving, or high-impact areas.
• Has 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.
• Is comfortable using empirical evidence, including evaluations, red-teaming results, deployment observations, and model failure modes, to inform policy decisions.
• Thinks in systems across policy, data, graders, classifiers, training, deployment safeguards, measurement, monitoring, and escalation workflows.
• Has technical judgment about what model behavior can realistically be trained, measured, evaluated, and enforced at scale.
• Works well across research, engineering, product, policy, domain experts, and operational teams.

Key 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.