other
Posted Jun 16, 2025RE / RS - Foundations, Search
at openai
San Francisco, United StatesHybrid
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Responsibilities
- - Collaborate with 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.
Requirements
- This work will support retrieval across many OpenAI products and internal research efforts, with opportunities for scientific publication and deep technical impact.
- 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.
- About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence
Benefits
- - 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.
Additional details
- About the Role We’re looking for a researcher focused on our embedding retrieval efforts.
- You’ll work with a 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.
- We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
- Responsibilities - Tackle embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning.
- You Might Thrive in This Role If You Have - Proven