Lead Applied Scientist, Document Understanding

New Position: This position is open due to an existing vacancy to support our evolving business needs. Lead Applied Scientist, Document Understanding About the Role 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. About You You hold a PhD in Computer Science, AI, NLP, or a related field, with 8 years of post-degree industry experience taking NLP and document understanding systems from development to production at scale. You have hands-on depth across the full applied arc — model development, distillation, evaluation, and deployment. You publish, you mentor, and you measure success by what ships and performs in production. What You'll Do 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, AI, NLP, or a related field — required 8 years of post-degree industry experience shipping document understanding, information extraction, or knowledge graph systems into production — not research-only experience Publications at ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD, or equivalent Production Python and experience with PyTorch, Hugging Face Transformers, and DeepSpeed 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-LP2 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 builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance. Caree ... (truncated, view full listing at source)