Lead Applied Scientist, Search & Information Retrieval

Thomson Reuters

. Own the design, development, and production deployment of large-scale search and information retrieval systems powering Westlaw, Practical Law, CoCounsel, and next-generation Thomson Reuters search experiences .

Posted 10/14/2026full-timeRemote • New York • United States, CanadaSenior💰 $147,600 - $274,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing large-scale search and information retrieval systems, with a strong focus on ranking, relevance optimization, and evaluation methodologies. Proficient in leveraging advanced AI and machine learning techniques to enhance retrieval quality and user outcomes.

Highest-signal resume keywords

PhD In Computer ScienceProduction Python SkillsExperience With PyTorchKnowledge Of Information RetrievalExperience With Elasticsearch

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Information RetrievalRanking SystemsSemantic RetrievalHybrid RetrievalQuery UnderstandingRelevance OptimizationSearch EvaluationIngestion PipelinesLLM-Based ApproachesDistributed Model Development

Soft Skills

MentoringTechnical Leadership

Tools & Technologies

Hugging Face TransformersElasticsearchOpenSearchSolrVespaAzureMLAWS SageMaker

Industry Keywords

Legal DomainTax DomainEnterprise ContentText-Rich ContentSearch Architecture

Tech Stack

Tools & technologies

AWSElasticSearchPythonPyTorch

About the role

Key responsibilities & impact

Own the design, development, and production deployment of large-scale search and information retrieval systems powering Westlaw, Practical Law, CoCounsel, and next-generation Thomson Reuters search experiences
Design and deploy search architectures for 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 around 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 through shipped, production-performing systems

Requirements

What you’ll need

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
Experience with PyTorch, Hugging Face Transformers, and distributed model development
Knowledge of search engine architecture, indexing systems, and ingestion pipelines
Experience building ranking and re-ranking systems
Knowledge of information retrieval, semantic retrieval, hybrid retrieval, and vector search architectures
Knowledge of query understanding, relevance optimization, and search evaluation methodologies
Experience with retrieval systems supporting large collections of text-rich content
Experience with LLM-enhanced retrieval, RAG architectures, and retrieval optimization
Experience with end-to-end measurement and evaluation of search quality and user outcomes
Experience with legal, regulatory, tax, scientific, or other text-heavy domains preferred
Experience building retrieval systems over large enterprise knowledge repositories preferred
Experience with Elasticsearch, OpenSearch, Solr, Vespa, or similar search technologies preferred
API platform development and self-service search platforms experience preferred
Experience with agentic AI systems incorporating retrieval capabilities preferred
Experience with AzureML or AWS SageMaker preferred
Experience building systems combining search, retrieval, and document understanding capabilities preferred

Benefits

Comp & perks

Flexible work arrangements, including work from anywhere for up to 8 weeks per year
Flexible vacation
Two company-wide Mental Health Days off
Headspace app access
Retirement savings
Tuition reimbursement
Employee incentive programs
Resources for mental, physical, and financial wellbeing
Two paid volunteer days off annually
Comprehensive health, dental, vision, disability, and life insurance programs (United States)
401k plan with company match (United States)
Sick and safe paid time off
Paid holidays
Parental leave
Sabbatical leave
Flexible Spending and Health Savings Accounts
Fitness reimbursement
Employee Assistance Program
Group Legal Identity Theft Protection benefit
529 Plan access
Commuter benefits
Adoption & Surrogacy Assistance
Employee Stock Purchase Plan
Potential eligibility for an Annual Bonus based on enterprise and individual performance