Job description

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. Pushing the boundaries of data, scaling laws, optimization techniques, model architectures, and efficiency improvements to propel our science. The Search team sits within Foundations, building agentic search by co-designing model–system interfaces with the core search stack (serving, indexing, retrieval) to translate model intent into reliable, real-world actions. Operating at the frontier of AI and information retrieval, the team develops large-scale systems that transform and index vast corpora, enabling models to reason over global knowledge and act dependably. In close partnership with researchers, we rapidly bring modeling breakthroughs into production and redefine how intelligent systems discover, retrieve, and synthesize information at planetary scale. About the Role We’re looking for a researcher focused on our search 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. 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 - 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. 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 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 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, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement cdn.openai.com. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form form.asana.com. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link form.asana.com. OpenAI Global Applicant Privacy Policy cdn.openai.com At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Requirements

Must-haves

About the Team
This work will support retrieval across many OpenAI products and internal research efforts, with opportunities for scientific publication and deep technical impact.
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.
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 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.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aimlmachine learning

Platform: ashby
Effort: quick

Benefits

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Vision