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

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