Lead Applied Scientist, Document Understanding

Thomson Reuters•Frisco, NY

About The Position

This role is part of the applied science function and owns the design, development, and production deployment of document understanding systems powering Westlaw, PracticalLaw, and CoCounsel. The work spans semantic chunking, document enrichment, knowledge graph construction, and synthetic data generation for complex legal, tax, and accounting content. The systems are used by multiple product teams and are expected to deliver reliable, measurable impact at scale. The Lead Applied Scientist makes technical decisions on architecture, chunking strategy, classification, and knowledge extraction; partners with engineering to deliver reliable systems across product lines; and provides technical input to senior leadership on AI strategy and roadmap. The role also mentors applied scientists and ML practitioners. This posting is for proactive recruitment and may be used to fill current or future vacancies.

Requirements

PhD in Computer Science, Artificial Intelligence, Natural Language Processing, or a related field.
At least 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 and 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.
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, including the ability to influence technical and business stakeholders.
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 small language model 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.

Nice To Haves

Legal document understanding, legal information extraction, or legal AI experience.
Experience with complex document structures, including nested hierarchies, cross-references, and non-uniform formatting.
Experience with retrieval or question-answering systems over large document collections.
Experience with RAG and agentic workflows in enterprise settings.
Experience with knowledge graph frameworks for legal or enterprise applications.
Experience with AzureML or AWS SageMaker.

Responsibilities

Design and deploy semantic chunking models for lengthy, non-uniformly structured legal documents, with adjustable granularity for different use cases.
Build document enrichment systems using legal and customer-defined taxonomies.
Develop LLM-based knowledge graph construction pipelines to 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 small language models.
Design component-level and end-to-end evaluation frameworks using expert annotation and synthetic data.
Own technical decisions concerning architecture, chunking strategy, classification approaches, 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.

Benefits

Flexible workplace policies and 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 access to the Headspace app.
Retirement savings plan, including a competitive 401(k) with company match for eligible US employees.
Tuition reimbursement.
Employee incentive programs and potential eligibility for an annual bonus based on enterprise and individual performance.
Health, dental, vision, disability, and life insurance programs for US employees.
Paid holidays, including two company mental health days off.
Parental leave and sabbatical leave.
Flexible Spending and Health Savings Accounts.
Fitness reimbursement and access to an Employee Assistance Program.
Commuter benefits.
Adoption and surrogacy assistance.
Employee Stock Purchase Plan.
Two paid volunteer days off annually and opportunities to participate in pro-bono consulting projects and ESG initiatives.
Resources for mental, physical, and financial wellbeing.