Lead Applied Scientist, Document Understanding
Core
Design, develop, and production deploy document understanding systems powering Westlaw, PracticalLaw, and CoCounsel, focusing on semantic chunking, document enrichment, and knowledge graph construction.
Role type
Senior Applied Scientist (Document Understanding & NLP)
Builds
Document understanding systems, semantic chunking models, knowledge graphs, and production-ready SLMs for legal, tax, and accounting content.
Domain
Legal Technology / Applied NLP / Knowledge Graphs
Deliverable
production ML models
Required skills
PhD in CS/AI/NLP, 8+ years experience in document understanding/IE/KG, end-to-end ML lifecycle expertise, Python, PyTorch, Hugging Face Transformers, DeepSpeed, research publication record, technical mentorship
Preferred skills
Legal document understanding, complex document structures, retrieval/QA systems, RAG/agentic workflows, AzureML/AWS SageMaker
Technologies
PyTorch, Hugging Face Transformers, DeepSpeed, AzureML, AWS SageMaker
Responsibilities
Design and deploy semantic chunking models for non-uniform legal documents; Build document enrichment systems using legal taxonomies; Develop LLM-based knowledge graph construction pipelines; Lead knowledge distillation efforts to compress large models; Design evaluation frameworks using expert annotation and synthetic data; Partner with engineering on delivery, reliability, and scale; Provide technical input to senior leadership on AI strategy; Mentor applied scientists and ML practitioners
Seniority
Senior, hands-on IC with mentorship responsibilities
