Lead Applied Scientist, Document Understanding

Thomson Reuters

Base: $147,600 - $274,200 usd (us); base: $140,000...
Not specified (assumed flexible remote work options)
Phd in computer science or related field
8+ years post-degree industry experience
Production deployment of nlp systems
Thomson Reuters is seeking a Lead Applied Scientist for their Document Understanding team, responsible for the design, development, and production deployment of systems that enhance legal, tax, and accounting content. The ideal candidate will possess a PhD in a relevant field and extensive experience in NLP and document understanding systems, with an emphasis on practical application and measurable outcomes

Job Summary

  • This role owns the design, development, and production deployment of document understanding systems powering Westlaw, PracticalLaw, and CoCounsel.
  • Candidates must have a PhD and 8+ years of industry experience shipping complex NLP and document understanding systems into production at scale.
  • The company offers comprehensive benefits including flexible work arrangements, two company-wide Mental Health Days off, and tuition reimbursement.

Matching Summary

Match Score: 85

Thomson Reuters is seeking a Lead Applied Scientist for their Document Understanding team, responsible for the design, development, and production deployment of systems that enhance legal, tax, and accounting content. The ideal candidate will possess a PhD in a relevant field and extensive experience in NLP and document understanding systems, with an emphasis on practical application and measurable outcomes.

Salary

Base: $147,600 - $274,200 USD (US); Base: $140,000 - $175,000 CAD (Ontario); Bonus: Eligible for Annual Bonus based on performance; Benefits: Comprehensive health, dental, vision, 401k match, flexible vacation, mental health days, tuition reimbursement

Skills & Requirements

Must-have

  • PhD in Computer Science or related field
  • 8+ years post-degree industry experience
  • Production deployment of NLP systems
  • Document layout analysis and semantic chunking
  • Knowledge graph construction from unstructured text
  • LLM-based information extraction and distillation
  • Python, PyTorch, Hugging Face Transformers

Nice-to-have

  • Legal document understanding experience
  • RAG and agentic workflows expertise
  • AzureML or AWS SageMaker proficiency
  • Experience with nested hierarchical documents
  • Publications at top-tier AI conferences

Key Requirements

  • PhD in Computer Science, AI, NLP, or related field
  • 8+ years of post-degree industry experience
  • Proven track record of shipping to production
  • Expertise in model distillation and SLM deployment

Work Rights

Not specified

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