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Principal Scientist- Search

Thomson Reuters
New York, New York Posted Jul 14, 2026
Hybrid

About this role

About the Role Lead the technical vision for AI-powered search, discovery, and knowledge systems at Thomson Reuters. Translate cutting-edge AI research into scalable production solutions that deliver measurable customer value. Provide technical leadership across retrieval, ranking, RAG, and agentic AI while mentoring teams. What You'll Do

  • Define and drive the technical vision for AI-powered search, discovery, and knowledge systems.
  • Lead development of retrieval, ranking, semantic search, RAG, and agentic AI capabilities.
  • Design and optimize retrieval architectures using embeddings, reranking, hybrid search, knowledge graphs, and contextual retrieval techniques.
  • Establish evaluation frameworks and benchmarks for search quality, relevance, grounding, and answer quality.
  • Partner closely with Product and Engineering teams to translate customer needs into scalable AI solutions.
  • Provide technical leadership on AI strategy, roadmap planning, and investment decisions.
  • Mentor scientists and engineers while promoting best practices across the organization. What We're Looking For
  • A PhD in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, Information Retrieval, or a related field.
  • 8+ years of industry experience building and deploying production AI systems at scale.
  • Proven experience delivering search, retrieval, NLP, knowledge graph, recommendation, or Generative AI solutions.
  • A strong product mindset with the ability to translate complex customer problems into successful AI applications.
  • Experience leading cross-functional initiatives and influencing technical strategy across research, engineering, and product organizations.
  • Outstanding communication, collaboration, and stakeholder management skills.
  • Technical Expertise: Information Retrieval, Search Relevance, Semantic Search, Ranking, and Retrieval-Augmented Generation (RAG); Large Language Models, Agentic AI frameworks, tool-using systems.

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