SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
OpenAI's Foundations Research team is seeking a Research Scientist to work on knowledge graphs within the Search team. The Search team sits within Foundations and builds agentic search by co-designing model-system interfaces with the core search stack: serving, indexing, and retrieval.
You will 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 research questions include: What structure helps models find and use information? How can we extract it reliably at web scale? How do we measure whether it improves the model's ability to reason and answer questions? And 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.
Key responsibilities include:
- Developing methods to extract and connect structured information across diverse web sources, including entities, attributes, and relationships
- Building scalable approaches for transforming noisy, inconsistent content into useful knowledge representations
- Exploring how models and compute can improve extraction quality and reduce manual work in building knowledge systems
- Designing evaluations that measure both the quality of extracted knowledge and its usefulness for retrieval and model reasoning
- Partnering with researchers and engineers to integrate structured knowledge into Search and using model performance to guide improvements
Requirements:
- 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
- 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 initial idea through experimentation and implementation
- First-principles approach to using AI and compute, with willingness to rethink established methods as model capabilities improve
- Strong interest in how the representation of information shapes what models can find, understand, and use
