RE / RS - Foundations, Search
San Francisco • FullTime
Posted 1y ago
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
We are seeking a researcher focused on embedding retrieval efforts to join our Foundations Research team. This team works on high-risk, high-reward ideas that could shape the future of AI, focusing on advancing the science and data for frontier models. You will collaborate with world-class research scientists and engineers to develop foundational technology enabling 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. Your work will support retrieval across many OpenAI products and internal research efforts, with opportunities for scientific publication and deep technical impact.
Responsibilities
- Tackle embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning.
- Collaborate with 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.