Research Engineer / Research Scientist - Foundations Retrieval Lead

Details

About the Team

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. We push the boundaries of data, scaling laws, optimization techniques, model architectures, and efficiency improvements to propel our science.

About the Role

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.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

Responsibilities

• 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 / You Might Thrive in This Role If You Have

• 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 large language models 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 all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer. Background checks for applicants will be administered in accordance with applicable law. We are committed to providing reasonable accommodations to applicants with disabilities.

Compensation & Benefits

• Base salary range listed for this role: $460K - $555K (offers equity and additional compensation components). The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience.
• Medical, dental, and vision insurance, HSA contributions, retirement plan with employer match.
• Paid parental leave, paid time off, company holidays, mental health and wellness support.
• Annual learning and development stipend, daily meals in offices, and relocation support for eligible employees.