Product Manager, Core Models

OpenAI OpenAI · AI Frontier · San Francisco, CA · Product Management

Product Manager for Core Models team at OpenAI, shaping how frontier models are built, measured, and launched. This role bridges Research, Engineering, and Product to translate user needs into model requirements, system architecture, and research priorities, focusing on areas like query understanding, data flywheels, evaluations, and inference. The goal is to build closed learning loops and reusable platforms for evaluation and experimentation, ultimately shipping AI products and capabilities to users.

What you'd actually do

  1. 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.
  2. Build closed learning loops that turn product usage, explicit feedback, and other user signals into datasets, evaluations, experiments, training priorities, and launch decisions.
  3. Define success across offline evaluations and online product metrics, balancing model quality, usefulness, latency, safety, reliability, and cost.
  4. Partner closely with post-training research, applied product engineering, Model Design, and Data Science to integrate capabilities into the mainline model stack.
  5. Create reusable platforms and operating systems for evaluation, experimentation, and signal collection so that new capabilities improve faster over time.

Skills

Required

  • Product Management
  • Technical Depth
  • Systems Rigor
  • Understanding of ML concepts
  • Data Analysis
  • Experimentation Design
  • Communication
  • Problem Solving
  • Ambiguity Tolerance

Nice to have

  • Search and Information Retrieval
  • Recommendation Systems
  • Personalization Systems
  • ML Platforms
  • Large-scale Data Systems
  • Model Evaluation
  • AI Product Infrastructure
  • Consumer Product Taste

What the JD emphasized

  • 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
  • pair offline evaluation with online experimentation and user signals
  • 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
  • Move quickly in ambiguous environments
  • communicate directly
  • help teams make sound decisions without waiting for perfect information

Other signals

  • product management
  • core models
  • research
  • engineering
  • model design
  • data science
  • product
  • model planning
  • launches
  • data flywheels
  • evaluations
  • measurement systems
  • capabilities
  • behavior
  • user needs
  • model requirements
  • system architecture
  • research priorities
  • query understanding
  • indexing
  • retrieval
  • ranking
  • tool boundaries
  • data
  • training
  • inference
  • evaluation
  • closed learning loops
  • product usage
  • explicit feedback
  • user signals
  • datasets
  • experiments
  • training priorities
  • launch decisions
  • offline evaluations
  • online product metrics
  • model quality
  • usefulness
  • latency
  • safety
  • reliability
  • cost
  • post-training research
  • applied product engineering
  • Model Design
  • Data Science
  • mainline model stack
  • reusable platforms
  • operating systems
  • experimentation
  • signal collection
  • product failures
  • emerging user needs
  • gaps
  • hypotheses
  • research investment
  • product investment
  • technically complex products
  • platforms
  • search and information retrieval
  • recommendation or personalization systems
  • ML platforms
  • large-scale data systems
  • model evaluation
  • AI product infrastructure
  • offline evaluation
  • online experimentation
  • user signals
  • useful metric
  • convenient one
  • researchers
  • engineers
  • technical depth
  • crisp judgment
  • consumer product taste
  • systems rigor
  • infrastructure
  • repeatable
  • ambiguous environments
  • sound decisions
  • perfect information
  • increasingly capable models
  • humility
  • judgment
  • responsibility
  • AI research and deployment company
  • general-purpose artificial intelligence
  • AI systems
  • safety
  • human needs
  • AI is an extremely powerful tool
  • safety and human needs at its core
  • different perspectives
  • voices
  • experiences
  • full spectrum of humanity