Model Policy Manager, Multimodal Safety

About the Role

We are hiring a Model Policy Manager to focus on the safety of multimodal models. In this role, you will shape how OpenAI identifies, evaluates, and addresses risks in multimodal AI models - such as GPT-Live and ChatGPT Images - as well as multimodal capabilities in frontier AI models.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Design and maintain model policies for audio, image, video, and omni-modal behavior.
• Translate theories of harm and threat models into behavioral safety policies, evaluation criteria, grading guidance, and safeguards.
• Identify and analyze safety regressions and failure patterns to identify gaps in existing policies and inform policy iteration.
• Develop policy artifacts that support model training, evaluation, and deployment, including behavior instructions, human-data campaigns, golden sets, and evaluations.
• Partner with AI researchers, domain experts, and product teams to operationalize policy into measurable model behavior.

You might thrive in this role if you:

• Have strong judgment about the real-world risks of advanced multimodal AI systems.
• Possess experience turning ambiguous safety questions into clear data-driven policies, behavioral boundaries, and measurable evaluation criteria.
• Treat policy as an end-to-end, measurable system by testing whether it produces the intended model behavior and diagnosing gaps across policy, data, graders, and safeguards.
• Leverage strong technical judgement to design policies around model behavior that can realistically be trained, measured, and supervised at scale.
• Demonstrate strong technical fluency and uses AI tools to accelerate policy development, evaluate model behavior, analyze failure patterns, and turn findings into actionable improvements.
• Are comfortable working hands-on with model data and evaluation results, including inspecting examples, analyzing failure patterns, assessing data quality, and distinguishing policy failures from grader, model, or system failures.
• Enjoy fast-paced, collaborative research environments where priorities shift as models, evidence, and risks change.
• Take a pragmatic, evidence-driven approach to reducing risk while preserving beneficial uses of AI.
• Have hands-on experience driving consensus and action in ambiguous spaces.