The opportunity
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.
What you'll do
Tackle embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning.
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.
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.
Proven experience leading high-performance teams of researchers or engineers: in ML infrastructure or foundational research.
What they're looking for
- 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.
