About the role
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, model + search co-design 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, and retrieval. We research and develop large-scale systems that transform and index vast corpora, enabling models to most effectively find and reason over information.
About the Role
We’re looking for a Researcher to work on semi-structured knowledge graphs within Search. You’ll research and develop methods for mining structured information from heterogeneous, unstructured and semi-structured web content, making that information more accessible and useful to AI models.
The core questions are: What structure helps models find and use information? How can we extract it reliably at web scale? And how do we measure whether it improves the model’s ability to reason and answer questions? How do we design the system to be optimizable end-to-end?
You’ll work across research, data, and systems, exploring how advances in AI and compute can improve the way knowledge is extracted, organized, and retrieved. This work will support retrieval across OpenAI products and internal research efforts, with opportunities for scientific publication.
Responsibilities
- Develop methods to extract and connect structured information across diverse web sources, including entities, attributes, and relationships.
- Build scalable approaches for transforming noisy, inconsistent content into useful knowledge representations.
- Explore how models and compute can improve extraction quality and reduce the manual work involved in building knowledge systems.
- Design evaluations that measure both the quality of extracted knowledge and its usefulness for retrieval and model reasoning.
- Partner with researchers and engineers to integrate structured knowledge into Search and use model performance to guide improvements.
You Might Thrive in This Role If You Have
- Deep experience in knowledge extraction, knowledge graphs, entity resolution, or related work that derives structure from large, heterogeneous datasets.
- Strong machine learning and research skills, with experience developing and evaluating methods for real-world data.
- A track record of building or scaling data and ML systems, particularly for web-scale extraction, indexing, or retrieval.
- Experience independently driving open-ended technical problems from an initial idea through experimentation and implementation.
- A first-principles approach to using AI and compute, with a willingness to rethink established methods as model capabilities improve.
- A strong interest in how the representation of information shapes what models can find, understand, and use.
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.
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.
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