Lead Applied Scientist, Document Understanding (Remote)

Thomson Reuters; New York (NY); On-site; USD 178,000 - 243,000; Full time

Benefits offered by this job

Headspace app

Tuition reimbursement

Two mental health days off

Stock purchase plan

Volunteer days

Job summary

Thomson Reuters is seeking an Lead Applied Scientist to own end-to-end document understanding systems powering Westlaw, PracticalLaw, and CoCounsel. You will design, build, and deploy semantic chunking, document enrichment, and knowledge graphs at scale across legal content.

You will mentor applied scientists, drive AI strategy, and partner with product teams to deliver measurable impact. This role requires leadership, deep ML expertise, and production-grade systems experience.

Qualifications

• PhD in Computer Science, AI, NLP or related field.
• 8+ years building and scaling document understanding or NLP systems.
• Strong Python and ML lifecycle experience.
• Track record of publications in top conferences.

Responsibilities

• Design and deploy semantic chunking for long legal documents.
• Build document enrichment systems using taxonomies.
• Develop knowledge graphs from legal content across products.
• Lead distillation to production-ready models and latency control.
• Collaborate with engineering on reliability and scale.

Skills

Document understanding

NLP

ML lifecycle

Python

PyTorch

Hugging Face Transformers

DeepSpeed

Research publication

Education

PhD in CS/AI/NLP

Tools

PyTorch

Hugging Face Transformers

DeepSpeed

Job description

Thomson Reuters is seeking an Lead Applied Scientist to own end-to-end document understanding systems powering Westlaw, PracticalLaw, and CoCounsel. You will design, build, and deploy semantic chunking, document enrichment, and knowledge graphs at scale across legal content.

You will mentor applied scientists, drive AI strategy, and partner with product teams to deliver measurable impact. This role requires leadership, deep ML expertise, and production-grade systems experience.