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[Remote] Senior Applied Scientist, Document Understanding

Work from home Full-time role Hiring

Note: The job is a remote job and is open to candidates in USA. Thomson Reuters is a global leader in providing trusted content and technology to professionals across various sectors. They are seeking a Senior Applied Scientist focused on designing and deploying document understanding systems that enhance their legal products. The role involves working on semantic chunking, document enrichment, and knowledge graph construction to deliver foundational intelligence for multiple product teams.

Responsibilities

  • Design and deploy semantic chunking models for lengthy, non-uniformly structured legal documents with adjustable granularity across use cases
  • Build document enrichment systems that classify documents according to legal and customer-defined taxonomies and extract rich metadata
  • Develop LLM-based knowledge graph construction pipelines that extract and link citations, entities, and legal concepts across diverse legal content
  • Build scalable synthetic data generation systems for model training, multi-hop query simulation, and hallucination-free answer generation
  • Apply knowledge distillation techniques to compress large models into latency-constrained, production-ready SLMs
  • Design evaluation frameworks — component-level and end-to-end — using expert annotation and synthetic data
  • Drive independent technical decisions on chunking strategy, classification approach, knowledge extraction methods, and multi-document reasoning architecture
  • Partner with engineering on delivery, reliability, and scale across multiple product lines
  • Contribute to published research at venues such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, and KDD, and to intellectual property

Skills

  • PhD or Master's in Computer Science, AI, NLP, or a related field
  • 5+ years of post-degree industry experience shipping document understanding, information extraction, or knowledge graph systems into production — not research-only experience
  • Publications at ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD, or equivalent
  • Experience leading through influence in an applied research setting
  • Production Python and experience with PyTorch, Hugging Face Transformers, and DeepSpeed
  • Document layout analysis and semantic chunking beyond fixed-size or paragraph-based methods
  • Hierarchical, multi-label document classification with domain-specific and customer-defined schemas
  • Entity recognition and linking, relation extraction, citation parsing, and knowledge graph construction from unstructured text
  • LLM-based information extraction, few-shot and multi-task learning, and post-training
  • Knowledge distillation, model compression, and SLM deployment under latency constraints
  • Synthetic data generation for NLP: query-answer generation with verification and scalable data augmentation
  • Annotation workflow design and evaluation framework development for document understanding tasks
  • Legal document understanding, legal information extraction, or legal AI applications
  • Complex document structures common in legal content: nested hierarchies, cross-references, non-uniform formatting, and embedded elements
  • Retrieval, QA, or analysis systems over large document collections
  • Knowledge graph frameworks for legal or enterprise applications
  • RAG and agentic workflows for enterprise knowledge systems
  • AzureML or AWS SageMaker

Benefits

  • Flexibility & Work-Life Balance
  • Career Development and Growth
  • Industry Competitive Benefits
  • Culture
  • Social Impact
  • Making a Real-World Impact
  • Market competitive health, dental, vision, disability, and life insurance programs
  • Competitive 401k plan with company match
  • Competitive vacation, sick and safe paid time off
  • Paid holidays (including two company mental health days off)
  • Parental leave
  • Sabbatical leave
  • Optional hospital, accident and sickness insurance paid 100% by the employee
  • Optional life and AD&D insurance paid 100% by the employee
  • Flexible Spending and Health Savings Accounts
  • Fitness reimbursement
  • Access to Employee Assistance Program
  • Group Legal Identity Theft Protection benefit paid 100% by employee
  • Access to 529 Plan
  • Commuter benefits
  • Adoption & Surrogacy Assistance
  • Tuition Reimbursement
  • Access to Employee Stock Purchase Plan

Company Overview

  • Thomson Reuters (TSX/NDAQ: TRI) informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. It was founded in 2008, and is headquartered in Toronto, Ontario, CAN, with a workforce of 10001+ employees. Its website is https://www.tr.com.
  • Company H1B Sponsorship

  • Thomson Reuters has a track record of offering H1B sponsorships, with 1 in 2026, 13 in 2025, 12 in 2024, 5 in 2023. Please note that this does not guarantee sponsorship for this specific role.
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