Lead Applied Scientist, Search & Information Retrieval

Thomson Reuters•Toronto, ON

1d•$147,600 - $274,200•Remote

About The Position

This role sits within the applied science function. You will own the design, development, and production deployment of large-scale search and information retrieval systems that power Westlaw, Practical Law, CoCounsel, and next-generation Thomson Reuters search experiences. The problems are real, the scale is large, and the expectation is shipped, reliable, measurable impact. You will work across retrieval architectures, indexing pipelines, ranking and re-ranking systems, semantic retrieval, hybrid search, and retrieval optimization for complex legal, tax, and accounting content. Multiple product teams depend on what this function delivers.

Requirements

PhD in Computer Science, Information Retrieval, AI, Machine Learning, NLP, or a related field preferred
8+ years of industry experience building production search, information retrieval, ranking, or recommendation systems
Publications at SIGIR, ACL, EMNLP, NeurIPS, ICLR, KDD, WWW, or equivalent venues
Strong production Python skills and experience with PyTorch, Hugging Face Transformers, and distributed model development
Hands-on production depth required in: Search engine architecture, indexing systems, and ingestion pipelines
Hands-on production depth required in: Ranking and re-ranking systems rather than solely consuming search technologies
Hands-on production depth required in: Information retrieval, semantic retrieval, hybrid retrieval, and vector search architectures
Hands-on production depth required in: Query understanding, relevance optimization, and search evaluation methodologies
Hands-on production depth required in: Retrieval systems supporting large collections of text-rich content
Hands-on production depth required in: LLM-enhanced retrieval, RAG architectures, and retrieval optimization
Hands-on production depth required in: End-to-end measurement and evaluation of search quality and user outcomes

Nice To Haves

Experience with legal, regulatory, tax, scientific, or other text-heavy domains
Building retrieval systems over large enterprise knowledge repositories
Experience with Elasticsearch, OpenSearch, Solr, Vespa, or similar search technologies
API platform development and self-service search platforms
Agentic AI systems that incorporate retrieval capabilities
AzureML or AWS SageMaker
Experience building systems that combine search, retrieval, and document understanding capabilities

Responsibilities

Design and deploy search architectures supporting large-scale legal, tax, and enterprise content collections
Build and optimize ingestion pipelines that analyze, enrich, and prepare documents for retrieval
Develop ranking and re-ranking systems using both traditional IR techniques and modern LLM-based approaches
Improve retrieval quality through semantic retrieval, hybrid retrieval, query understanding, and relevance optimization
Design evaluation frameworks for retrieval performance, relevance, ranking quality, and end-user outcomes
Lead technical decisions around indexing strategies, retrieval architectures, ranking models, and search infrastructure
Partner with engineering teams to deliver scalable, reliable, and performant search services
Contribute to the development of self-service search platform capabilities used by internal product teams
Provide technical input to senior leadership on search, retrieval, and AI strategy
Mentor applied scientists and machine learning practitioners across the organization