Research Scientist / Research Engineer, Search Integrity

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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. In close partnership with researchers, we bring modeling advances into production and improve how AI systems discover, retrieve, and use knowledge.

ABOUT THE ROLE

We’re looking for a Researcher to lead Search Integrity: the systems that distinguish useful web content from spam, manipulation, and other adversarial or low-quality material.

The challenge extends beyond evaluating individual pages. A page may appear credible in isolation, while its links, metadata, or relationships with other sites reveal coordinated manipulation. As AI makes convincing content easier to produce at scale, detection systems must adapt to increasingly sophisticated adversaries.

You’ll define the technical approach, build classifiers and evaluations, and shape how integrity signals influence what Search indexes and retrieves. Working closely with Search and broader Safety and Integrity teams, you’ll use frontier models to advance detection methods and bring them into production.

RESPONSIBILITIES

• Define the technical direction for Search Integrity, identifying gaps and determining which problems and interventions to prioritize.

• Develop AI and ML systems to detect spam, scams, phishing, malicious domains, prompt injections, and other adversarial or low-quality web content.