Lead Applied Scientist, Document Understanding

Thomson Reuters

. Own the design, development, and production deployment of document understanding systems powering Westlaw, PracticalLaw, and CoCounsel .

Posted 10/17/2026full-timeRemote • United States, CanadaSenior💰 $147,600 - $274,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying document understanding systems, with a strong focus on natural language processing and machine learning lifecycle management. Proven ability to mentor technical talent and drive impactful AI solutions in legal contexts.

Highest-signal resume keywords

PhD In Computer ScienceExpertise In Natural Language ProcessingStrong Python Programming SkillsHands-On Experience With PyTorchKnowledge Graph Construction

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Document UnderstandingInformation ExtractionKnowledge GraphModel DevelopmentModel EvaluationModel OptimizationKnowledge DistillationEntity RecognitionCitation ParsingSemantic Chunking

Soft Skills

Strong CommunicationCross-Functional CollaborationMentoring Technical Talent

Tools & Technologies

Hugging Face TransformersDeepSpeedAzureMLAWS SageMaker

Industry Keywords

Legal Document UnderstandingLegal AIComplex Document StructuresRetrieval SystemsQA Systems

Tech Stack

Tools & technologies

AWSPythonPyTorch

About the role

Key responsibilities & impact

Own the design, development, and production deployment of document understanding systems powering Westlaw, PracticalLaw, and CoCounsel
Design and deploy semantic chunking models for lengthy, non-uniformly structured legal documents
Build document enrichment systems using legal and customer-defined taxonomies
Develop LLM-based knowledge graph construction pipelines extracting and linking citations, entities, and legal concepts
Lead knowledge distillation efforts to compress large models into latency-constrained, production-ready SLMs
Design component-level and end-to-end evaluation frameworks using expert annotation and synthetic data
Own technical decisions on architecture, chunking strategy, classification approach, and knowledge extraction methods
Partner with engineering on delivery, reliability, and scale across multiple product lines
Provide technical input to senior leadership on AI strategy and roadmap
Mentor applied scientists and ML practitioners

Requirements

What you’ll need

PhD in Computer Science, Artificial Intelligence, Natural Language Processing, or a related field
8+ years of industry experience building and scaling document understanding, information extraction, knowledge graph, or applied NLP solutions
Expertise across the end-to-end ML lifecycle, including model development, evaluation, optimization, distillation, and production deployment
Strong Python programming skills
Hands-on experience with PyTorch, Hugging Face Transformers, and DeepSpeed
Track record of publishing research in leading conferences such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD, or equivalent
Ability to mentor technical talent and drive measurable business impact through production-ready AI systems
Experience translating advanced research into scalable, high-performing products and services
Strong communication and cross-functional collaboration skills
Hands-on production depth in document layout analysis and semantic chunking beyond fixed-size or paragraph-based methods
Hands-on production depth in hierarchical, multi-label document classification with domain-specific and customer-defined schemas
Hands-on production depth in entity recognition and linking, relation extraction, citation parsing, and knowledge graph construction from unstructured text
Hands-on production depth in LLM-based information extraction, few-shot and multi-task learning, and post-training
Hands-on production depth in knowledge distillation, model compression, and SLM deployment under latency constraints
Hands-on production depth in synthetic data generation and annotation workflow design
Hands-on production depth in end-to-end evaluation framework design for document understanding
Preferred: Legal document understanding, legal IE, or legal AI experience
Preferred: Complex document structures, retrieval or QA systems, RAG and agentic workflows, knowledge graph frameworks, AzureML or AWS SageMaker

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
Mental, physical, and financial wellbeing resources
Two paid volunteer days off annually
Health, dental, vision, disability, and life insurance programs
Competitive 401(k) plan with company match
Sick and safe paid time off
Paid holidays
Parental leave
Sabbatical leave
Optional hospital, accident and sickness insurance
Optional life and AD&D insurance
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
Annual Bonus based on enterprise and individual performance (role may be eligible)