Senior Applied Scientist, Document Understanding

Thomson Reuters

Base: $127,400 usd - $236,600 usd; bonus/equity: e...
Hybrid
Semantic chunking
Document enrichment
Knowledge graph construction
Thomson Reuters is seeking a Senior Applied Scientist specializing in Document Understanding to design and deploy systems that enhance legal, tax, and accounting content. The ideal candidate will have advanced expertise in NLP and document understanding, with a strong publication record and experience in production environments

Job Summary

  • Design and deploy document understanding systems that directly power Westlaw, PracticalLaw, and CoCounsel, focusing on 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.
  • Thomson Reuters offers a comprehensive benefits package including flexible vacation, mental health days, and career development programs.

Matching Summary

Match Score: 85

Thomson Reuters is seeking a Senior Applied Scientist specializing in Document Understanding to design and deploy systems that enhance legal, tax, and accounting content. The ideal candidate will have advanced expertise in NLP and document understanding, with a strong publication record and experience in production environments.

Salary

Base: $127,400 USD - $236,600 USD; Bonus/Equity: Eligible for Annual Bonus; Benefits: Comprehensive benefits package

Skills & Requirements

Must-have

  • semantic chunking
  • document enrichment
  • knowledge graph construction
  • synthetic data generation
  • LLM-based information extraction
  • model distillation
  • production Python
  • PyTorch
  • Hugging Face Transformers
  • DeepSpeed

Nice-to-have

  • legal document understanding
  • complex document structures
  • RAG and agentic workflows
  • enterprise applications

Key Requirements

  • PhD or Master's degree
  • 5+ years industry experience
  • Publications at top AI/NLP venues
  • Experience leading through influence
  • Document layout analysis
  • Hierarchical, multi-label document classification
  • Entity recognition and linking
  • LLM few-shot and multi-task learning
  • Model compression and SLM deployment
  • Synthetic data generation workflow design
  • End-to-end evaluation framework design

Work Rights

Not specified

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