Model Policy, Frontier Cyber Risk

Location

San Francisco

Full time

Hybrid

Safety Systems

• San Francisco $207K – $295K
  • Offers Equity

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(s) 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.

More details about our benefits are available to candidates during the hiring process.

This role is at‑will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

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.

Within Safety Systems, the Model Policy team aligns model behavior with desired human values and norms. We co‑design policy with models and for models by driving rapid policy taxonomy iteration based on data and defining evaluation criteria for foundational models’ ability to reason about safety.

Frontier AI systems are rapidly expanding what is possible in cybersecurity and software engineering. These capabilities create major defensive opportunities, but they also raise serious dual‑use and misuse risks across areas such as malware development, exploit discovery, vulnerability chaining, credential abuse, cyber intrusion, and autonomous offensive operations.

In this role, you will help define how OpenAI’s models should behave in high‑risk cybersecurity contexts. You will develop policy frameworks, threat models, taxonomies, evaluations, and behavioral specifications that guide model behavior across training, deployment, and monitoring systems. This role sits at the intersection of cybersecurity, AI safety, threat modeling, evaluation science, and policy implementation.

You will work closely with research, engineering, safety training, preparedness, and product teams to build policies that are technically grounded, measurable, enforceable, and responsive to real‑world cyber risk.

• Design and maintain model policies for cybersecurity and frontier‑risk domains, especially dual‑use and high‑risk cyber capabilities.
• Translate cybersecurity threat models into clear behavioral specifications, evaluation criteria, grading guidance, and system‑level mitigations.
• Define practical boundaries between legitimate security research, defensive workflows, and assistance that could materially enable harmful activity.
• Build policy artifacts that support implementation across training, evaluation, deployment, monitoring, and escalation systems.
• Partner with safety researchers, engineers, and evaluation teams to operationalize policies into scalable model behavior and…