Lead Applied Scientist, Document Understanding

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, Practical Law, 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

• 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

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 Deep Speed

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…