RE / RS - Foundations, Search

Responsibilities

• 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.

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.

Qualifications

• PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• At least 5 years of relevant research or engineering experience.
• Strong publication record in top-tier conferences and journals.

Skills

• Experience with large-scale machine learning systems.
• Knowledge of transformer-based models and their applications.
• Expertise in representation learning and embedding models.
• Experience with vector retrieval systems and metric learning.

Benefits

• Hybrid work model of 3 days in the office per week.
• Relocation assistance for new employees.

Pay

Compensation Range: $445K - $555K

Schedule

Hybrid work model of 3 days in the office per week.