Lead Applied Scientist, Search & Information Retrieval

Thomson Reuters•United States of America, NY

About The Position

This role is part of the applied science function and owns the design, development, and production deployment of large-scale search and information retrieval systems supporting Westlaw, Practical Law, CoCounsel, and future Thomson Reuters search experiences. The work spans retrieval architectures, indexing pipelines, ranking and re-ranking systems, semantic and hybrid search, and retrieval optimization for complex legal, tax, and accounting content. The role is expected to deliver reliable, measurable systems in production and works across multiple product teams. The Lead Applied Scientist provides technical leadership on search architectures, indexing strategies, ranking models, and infrastructure; partners with engineering teams to deliver scalable search services; contributes to self-service search platform capabilities; and advises senior leadership on search, retrieval, and AI strategy. The role also publishes research, mentors applied scientists and machine learning practitioners, and evaluates success through shipped systems and production performance. Thomson Reuters describes its work as helping customers pursue justice, truth, and transparency through trusted content and technology. The company has 26,000 employees across more than 70 countries. This posting is for proactive recruitment and may be used to fill current or future vacancies. The posting closes 03/31/2027. The role may also be eligible for an annual bonus based on enterprise and individual performance.

Requirements

PhD in Computer Science, Information Retrieval, AI, Machine Learning, NLP, or a related field; the posting lists a PhD as preferred in the Required Qualifications section.
At least 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.
Experience with search engine architecture, indexing systems, and ingestion pipelines.
Experience building ranking and re-ranking systems, rather than solely consuming search technologies.
Knowledge of information retrieval, semantic retrieval, hybrid retrieval, and vector search architectures.
Experience with query understanding, relevance optimization, and search evaluation methodologies.
Experience with retrieval systems supporting large collections of text-rich content.
Knowledge of LLM-enhanced retrieval, RAG architectures, and retrieval optimization.
Experience with 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.
Experience building retrieval systems over large enterprise knowledge repositories.
Experience with Elasticsearch, OpenSearch, Solr, Vespa, or similar search technologies.
Experience with API platform development and self-service search platforms.
Experience with agentic AI systems that incorporate retrieval capabilities.
Experience with 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 traditional information retrieval 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 about indexing strategies, retrieval architectures, ranking models, and search infrastructure.
Partner with engineering teams to deliver scalable, reliable, and performant search services.
Contribute to 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.
Publish research and measure success by systems shipped and their production performance.

Benefits

Flexible work arrangements, including work from anywhere for up to 8 weeks per year.
Flexible vacation and competitive vacation, sick and safe paid time off.
Two company-wide Mental Health Days off annually and paid holidays.
Access to the Headspace app and Employee Assistance Program.
Retirement savings and a competitive 401(k) plan with company match.
Tuition reimbursement.
Employee incentive programs and possible annual bonus.
Health, dental, vision, disability, and life insurance programs.
Parental leave and sabbatical leave.
Flexible Spending and Health Savings Accounts.
Fitness reimbursement.
Commuter benefits.
Adoption and surrogacy assistance.
Employee Stock Purchase Plan.
Optional hospital, accident and sickness insurance; optional life and AD&D insurance; and group legal and identity theft protection, paid 100% by the employee.
Two paid volunteer days off annually.
Resources for mental, physical, and financial wellbeing.
Access to professional development and continuous learning programs.