Description
Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.
About the Team
Safety Systems manages the complete lifecycle of safety efforts for OpenAI’s frontier models, ensuring our models are deployed responsibly and have a positive impact on society. Our work spans diverse research and engineering initiatives,from system-level safeguards and model training to evaluation and red-teaming,all aimed at mitigating misuse, misalignment, and maintaining our high bar for safety. We lead OpenAI's commitment to developing and deploying safe Artificial General Intelligence (AGI), fostering a culture of trust, responsibility, and transparency. Our goal is to continuously learn from deployments, distribute AI’s benefits widely, and ensure that powerful tools remain aligned with human values and safety considerations.
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.
Responsibilities
- 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.
Requirements
- 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.