SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
OpenAI's Safety Systems team is 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.
You will design and maintain model policies for audio, image, video, and omni-modal behavior. Your work will translate theories of harm and threat models into behavioral safety policies, evaluation criteria, grading guidance, and safeguards. You'll identify and analyze safety regressions and failure patterns to identify gaps in existing policies and inform policy iteration.
You will develop policy artifacts that support model training, evaluation, and deployment, including behavior instructions, human-data campaigns, golden sets, and evaluations. You'll partner with AI researchers, domain experts, and product teams to operationalize policy into measurable model behavior.
The role is based in San Francisco with a hybrid work model of 3 days in the office per week. OpenAI offers relocation assistance to new employees.
QUALIFICATIONS:
- Strong judgment about the real-world risks of advanced multimodal AI systems
- Experience turning ambiguous safety questions into clear data-driven policies, behavioral boundaries, and measurable evaluation criteria
- Ability to treat policy as an end-to-end, measurable system by testing whether it produces intended model behavior and diagnosing gaps across policy, data, graders, and safeguards
- Strong technical judgment to design policies around model behavior that can realistically be trained, measured, and supervised at scale
- Strong technical fluency and ability to use AI tools to accelerate policy development, evaluate model behavior, analyze failure patterns, and turn findings into actionable improvements
- Comfort 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
- Ability to thrive in fast-paced, collaborative research environments where priorities shift as models, evidence, and risks change
- Pragmatic, evidence-driven approach to reducing risk while preserving beneficial uses of AI
- Hands-on experience driving consensus and action in ambiguous spaces
