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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?

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