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OpenAI Logo OpenAI · IT - Software Development

Research Engineer / Research Scientist - Foundations Retrieval Lead

United States, San Francisco 445000.00 - 555000.00 USD / Year · Job Posted February 21, 2026
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Job Description

The Foundations Research team works on high-risk, high-reward ideas that could shape the next decade of AI. Our goal is to advance the science and data that enable our training and scaling efforts, with a particular focus on future frontier models. Pushing the boundaries of data, scaling laws, optimization techniques, model architectures, and efficiency improvements to propel our science. We’re looking for a technical research lead to grow and lead our embeddings-focused retrieval efforts. You’ll manage a team of world-class research scientists and engineers developing foundational technology that enables models to retrieve and condition on the right information, at the right time. This includes designing new embedding training objectives, scalable vector store architectures, and dynamic indexing methods. This work will support retrieval across many OpenAI products and internal research efforts, with opportunities for scientific publication and deep technical impact.

Job Responsibility

  • Lead research into embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning
  • Manage a team of researchers and engineers building end-to-end infrastructure for training, evaluating, and integrating embeddings into frontier models
  • Drive innovation in dense, sparse, and hybrid representation techniques, metric learning, and learning-to-retrieve systems
  • Collaborate closely with Pretraining, Inference, and other Research teams to integrate retrieval throughout the model lifecycle
  • Contribute to OpenAI’s long-term vision of AI systems with memory and knowledge access capabilities rooted in learned representations

Requirements

  • Proven experience leading high-performance teams of researchers or engineers in ML infrastructure or foundational research
  • Deep technical expertise in representation learning, embedding models, or vector retrieval systems
  • Familiarity with transformer-based LLMs and how embedding spaces can interact with language model objectives
  • Research experience in areas such as contrastive learning, supervised or unsupervised embedding learning, or metric learning
  • A track record of building or scaling large machine learning systems, particularly embedding pipelines in production or research contexts
  • A first-principles mindset for challenging assumptions about how retrieval and memory should work for large models

What we offer

  • 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
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