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Applied Scientist, Search & Information Retrieval

Thomson Reuters
hybrid
Hybrid - United States (New York, NY; Frisco, TX; Ann Arbor, MI; Eagan, MN)

About this role

Job title: Applied Scientist, Search & Information Retrieval

About the Role This is an applied science position focused on building and deploying production-grade search systems that power Westlaw, PracticalLaw, and CoCounsel. You will work across neural information retrieval, semantic and hybrid search, re-ranking, and query understanding — delivering search quality and relevance at scale for legal and professional content.

What You'll Do

  • Design, build, and deploy end-to-end neural search systems including dense retrieval, hybrid search, semantic chunking, embedding models, cross-encoders, SLM re-rankers, and transformer-based approaches
  • Develop models for query understanding, document re-ranking, and retrieval quality optimisation
  • Build evaluation frameworks — component-level and end-to-end — using expert annotation and synthetic data generation

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