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.