Product Manager, Core Models

OpenAI•San Francisco, CA

4d•$347,000 - $490,000•Hybrid

About The Position

As a Product Manager for the Core Models team, you'll 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. 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.

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.

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.