Backed by Greylock, Insight and Khosla.
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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.
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...
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