# Product Manager, Core Models

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** Product Management
**Salary:** $347k – $490k/yr
**Experience:** 5+ years
**Skills:** Product Management, AI Infrastructure, Model Evaluation, Search And Information Retrieval, Recommendation Systems, Ml Platforms, Large-Scale Data Systems, Offline Evaluation, Online Experimentation, Query Understanding, Retrieval And Ranking
**Posted:** 2026-07-07

> Product Manager on OpenAI's Core Models team, translating user needs into model requirements, building data flywheels and evaluation systems, and partnering with research and engineering to ship frontier AI capabilities. Requires deep PM expertise in technically complex AI/ML platforms.

## Job Description

## 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
- Deep expertise in product management or closely related experience, including ownership of technically complex products or platforms.
- 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.

## Nice-to-Haves
- Experience with AI model development, evaluation systems, or large-scale ML infrastructure.

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