# Lead Applied Scientist, Document Understanding at Thomson Reuters

Thomson Reuters is hiring a Lead Applied Scientist, Document Understanding for a remote role open to applicants in United States. It pays $148k-$274k a year and Level rates it Builds AI ●●●●; you can [apply on Level](https://jobsbylevel.com/go/564faa0f-d378-4204-b643-214ec13a00da?utm_medium=ai-agent).

AI Level 4, AI centrality 84 out of 100. Remote (United States of America, New York, New York).

## Details

- Company: [Thomson Reuters](https://jobsbylevel.com/companies/thomson-reuters)
- AI level: AI Level 4 (score 84 out of 100)
- Location: Remote (United States of America, New York, New York)
- Salary: $148k-$274k
- Posted: October 16, 2026
- Apply: https://jobsbylevel.com/go/564faa0f-d378-4204-b643-214ec13a00da?utm_medium=ai-agent

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/lead-applied-scientist-document-understanding-at-thomson-reuters-c9a6e0

## Description

Lead Applied Scientist, Document Understanding Job Description: This role sits within the applied science function. You will own the design, development, and production deployment of document understanding systems that directly power Westlaw, PracticalLaw, and CoCounsel. The problems are real, the scale is large, and the expectation is shipped, reliable, measurable impact.You will work across semantic chunking, document enrichment, knowledge graph construction, and synthetic data generation for complex legal, tax, and accounting content. Multiple product teams depend on what this function delivers. Key Responsibilities: Design and deploy semantic chunking models for lengthy, non-uniformly structured legal documents with adjustable granularity across use cases Build document enrichment systems using legal and customer-defined taxonomies Develop LLM-based knowledge graph construction pipelines that extract and link citations, entities, and legal concepts across diverse legal content Lead knowledge distillation efforts to compress large models into latency-constrained, production-ready SLMs Design evaluation frameworks — component-level and end-to-end — 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 on the team Required Qualifications: 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 Demonstrated expertise across the end-to-end ML lifecycle, including model development, evaluation, optimization, distillation, and production deployment Strong Python programming skills with hands-on experience in PyTorch, Hugging Face Transformers, and DeepSpeed Track record of publishing research in leading conferences such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD, or equivalent Proven 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, with the ability to influence technical and business stakeholders alike ​ Hands-on production depth required in: Document layout analysis and semantic chunking beyond fixed-size or paragraph-based methods Hierarchical, multi-label document classification with domain-specific and customer-defined schemas Entity recognition and linking, relation extraction, citation parsing, and knowledge graph construction from unstructured text LLM-based information extraction, few-shot and multi-task learning, and post-training Knowledge distillation, model compression, and SLM deployment under latency constraints Synthetic data generation and annotation workflow design End-to-end evaluation framework design for document understanding Preferred Qualifications Legal document understanding, legal IE, or legal AI experience Complex document structures: nested hierarchies, cross-references, non-uniform formatting Retrieval or QA systems over large document collections RAG and agentic workflows in enterprise settings Knowledge graph frameworks for legal or enterprise applications AzureML or AWS SageMaker #LI-SM2 This posting is for proactive recruitment purposes and may be used to fill current openings or future vacancies within our organization. What’s in it For You? Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This

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