Description
Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this role is $347K – $490K, with generous equity, performance-related bonuses 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
The Core Models team helps shape how OpenAI’s frontier models are built, measured, and launched. We work across Research, Engineering, Model Design, Data Science, and Product to turn advances in model capabilities into reliable, useful experiences for people. Our scope includes model planning and launches as well as building data flywheels, evaluations and measurement systems to ensure our models have strong capabilities and behavior.
About the Role
As a Product Manager for the Core Models team, you will be at the forefront of defining and guiding the future of how our AI models work in real-world applications. You will connect user needs to model and systems decisions: how prompts are understood; how information is aggregated and made useful for training and evaluation data; and how capabilities move from research prototypes into the mainline model and launch stack. You will operate comfortably across research, infrastructure, and consumer product surfaces, creating clarity where ownership and technical boundaries are still emerging.
Responsibilities
- Translate user and product goals into clear model requirements, system architecture choices, and research priorities across query understanding, indexing, retrieval, ranking, tool boundaries, data, training, inference, and evaluation.
- Build closed learning loops that turn product usage, explicit feedback, and other user signals into datasets, evaluations, experiments, training priorities, and launch decisions.
- Define success across offline evaluations and online product metrics, balancing model quality, usefulness, latency, safety, reliability, and cost.
- Partner closely with post-training research, applied product engineering, Model Design, and Data Science to integrate capabilities into the mainline model stack.
- Create reusable platforms and operating systems for evaluation, experimentation, and signal collection so that new capabilities improve faster over time.
- Use concrete product failures and emerging user needs to identify gaps, form hypotheses, and shape the next wave of research and product investment.
Requirements
- Have deep expertise in product management or closely related experience, including ownership of technically complex products or platforms.
- Bring deep fluency in one or more relevant domains: search and information retrieval, recommendation or personalization systems, ML platforms, large-scale data systems, model evaluation, or AI product infrastructure.
- Know how to pair offline evaluation with online experimentation and user signals, and can distinguish a useful metric from a convenient one.
- Earn the trust of researchers and engineers through technical depth, crisp judgment, and a willingness to engage directly with the details.
- Combine consumer product taste with systems rigor, and care about both the quality people feel and the infrastructure that makes it repeatable.
- Move quickly in ambiguous environments, communicate directly, and help teams make sound decisions without waiting for perfect information.
- Care deeply about how increasingly capable models affect people, and bring humility, judgment, and a strong sense of responsibility to the work.