Lead Applied Scientist, Document Understanding

Role 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.

• 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.

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

Requirements

• 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

Benefits

• Flex My Way: Supportive workplace policies for personal and professional responsibilities
• Flexible work arrangements, including work from anywhere for up to 8 weeks per year
• Career Development and Growth: Continuous learning and skill development culture
• Industry Competitive Benefits:
  • Comprehensive benefit plans including flexible vacation
  • Two company-wide Mental Health Days off
  • Access to the Headspace app
  • Retirement savings, tuition reimbursement, employee incentive programs
  • Resources for mental, physical, and financial wellbeing
• Globally recognized culture for inclusion and belonging
• Social Impact: Two paid volunteer days off annually and opportunities for pro-bono consulting projects
• Making a Real-World Impact: Helping customers pursue justice, truth, and transparency