Senior Applied Scientist, NLP​/GenAI

Document understanding is a foundational intelligence layer that powers every major capability across our legal AI platform—from search and information extraction to agentic reasoning in products like Westlaw, Practical Law, and CoCounsel. You will build state‑of‑the‑art semantic chunking, document enrichment, and knowledge graph construction systems that serve as the cognitive foundation multiple product teams depend on, working across authoritative legal, tax and accounting content and extraordinarily diverse customer data.

This is a rare opportunity to solve publishing‑quality research problems with immediate production impact—your innovations will directly shape how millions of legal professionals research, analyze, and reason over complex legal documents while advancing the capabilities that enable the next generation of intelligent legal AI agents.

• Innovate & Deliver: Design, build, test, and deploy end‑to‑end AI solutions for complex document understanding tasks in the legal domain. Develop advanced models for semantic chunking of lengthy, non‑uniformly structured legal documents with adjustable granularity levels for different use cases. Build document enrichment systems that classify documents according to legal and customer‑defined taxonomies and extract rich metadata. Create LLM‑based knowledge graph construction pipelines that extract and link heterogeneous legal knowledge including citations, entities, and legal concepts across diverse legal content.
  
  Develop scalable synthetic data generation systems to support model training, simulate complex legal research queries and generate hallucination‑free answers. Work in collaboration with engineering to ensure well‑managed software delivery and reliability at scale.
• Evaluate & Optimize: Develop comprehensive data and evaluation strategies for both component‑level and end‑to‑end quality, leveraging expert human annotation and synthetic data generation. Apply robust training and evaluation methodologies that balance model performance with latency requirements, particularly for SLM‑based solutions. Apply knowledge distillation techniques to compress large models into efficient SLMs suitable for production deployment.
• Drive Technical Decisions: Independently determine appropriate architectures for challenging document understanding problems including semantic chunking strategies, document classification approaches, LLM‑based knowledge extraction methods, and multi‑document reasoning architectures. Balance accuracy, efficiency, and scalability while solving real‑world challenges.
• Align & Communicate: Partner closely with Engineering and Product teams to translate complex legal document understanding challenges into scalable, production‑ready solutions. Engage stakeholders across multiple product lines to deeply understand use case requirements, shaping objectives that align document understanding capabilities with diverse business needs including next‑generation search and deep legal research.
• Advance the Field: Maintain scientific and technical expertise in one or more relevant areas as demonstrated through product deliverables, published research at top venues, and intellectual property.

• PhD in Computer Science, AI, NLP, or a related field, or a Master’s with equivalent research/industry experience.
• 5+ years of hands‑on experience building and deploying document understanding systems, information extraction pipelines, or knowledge graph construction using deep learning, LLMs and NLP methods.
• Proven ability to translate complex document understanding problems into innovative AI applications that balance accuracy and efficiency.
• Professional experience scaling yourself and leading through others, in an applied research setting.
• Strong programming skills (e.g., Python) and experience with modern deep learning frameworks (e.g., PyTorch, Hugging Face Transformers, Deep Speed).
• Publications at relevant venues such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD.

• Deep understanding of document understanding fundamentals: document layout analysis, semantic chunking approaches beyond fixed‑size or paragraph‑based…