Job Specifications
Lead Applied Scientist, Document Understanding
Document understanding is a foundational intelligence layer that powers every major capability across our legal AI platform—from search and information extraction to agentic reasoning in products like Westlaw, PracticalLaw, and CoCounsel. You'll build state-of-the-art semantic chunking, document enrichment, and knowledge graph construction systems that serve as the cognitive foundation multiple product teams depend on, working across authoritative legal, tax, and accounting content and extraordinarily diverse customer data.
This is a rare opportunity to solve publishing-quality research problems with immediate production impact—your innovations will directly shape how millions of legal professionals research, analyze, and reason over complex legal documents while advancing the capabilities that enable the next generation of intelligent legal AI agents.
About The Role
As a Lead Applied Scientist, you will:
Innovate & Deliver at Scale
Lead the design, build, test, and deployment of end-to-end AI solutions for complex document understanding tasks in the legal domain
Direct the execution of large-scale projects including: advanced semantic chunking models for lengthy, non-uniformly structured legal documents with adjustable granularity; document enrichment systems with legal and customer-defined taxonomies; LLM-based knowledge graph construction pipelines that extract and link heterogeneous legal knowledge; and scalable synthetic data generation systems
Serve as the technical lead and primary point of reference, ensuring full accountability for all research deliverables
Partner with engineering to guarantee well-managed software delivery and reliability at scale across multiple product lines
Evaluate, Optimize & Advance Capabilities
Design comprehensive evaluation strategies for both component-level and end-to-end quality, leveraging expert annotation and synthetic data
Apply robust training methodologies that balance performance with latency requirements
Lead knowledge distillation initiatives to compress large models into production-ready SLMs
Maintain scientific and technical expertise through product deliverables, published research, and intellectual property contributions
Inform Labs shared capabilities and research themes through novel approaches to challenging business problems
Drive Strategic Technical Direction
Independently determine appropriate architectures for complex document understanding challenges, balancing accuracy, efficiency, and scalability
Make critical technical decisions on semantic chunking strategies, document classification approaches, LLM-based knowledge extraction methods, and multi-document reasoning architectures
Provide input to business stakeholders, mid-to-senior level leadership, and Labs leadership on long-term AI strategy
Develop in-depth knowledge of TR customers and data infrastructure across multiple products to shape technical roadmaps
Align, Communicate & Lead
Partner closely with Engineering and Product teams to translate complex legal document understanding challenges into scalable, production-ready solutions
Engage stakeholders across multiple product lines to deeply understand use case requirements, shaping objectives that align document understanding capabilities with diverse business needs including next-generation search and deep legal research
Mentor and coach team members with varied ML/NLP abilities, building technical capability across the organization
About You
Required Qualifications
PhD in Computer Science, AI, NLP, or a related field, or a Master's degree with equivalent research/industry experience
7+ years of hands-on experience building and deploying document understanding systems, information extraction pipelines, or knowledge graph construction using deep learning, LLMs, and NLP methods
Proven ability to translate complex document understanding problems into innovative AI applications that balance accuracy and efficiency
Demonstrated ability to provide technical leadership, mentor team members, and influence without formal authority in an applied research setting
Strong programming skills (e.g., Python) and experience with modern deep learning frameworks (e.g., PyTorch, Hugging Face Transformers, DeepSpeed)
Publications at relevant venues such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, or KDD
Technical Qualifications
Deep understanding of document understanding fundamentals: document layout analysis, semantic chunking approaches beyond fixed-size or paragraph-based methods, document classification handling hierarchical taxonomies, imbalanced multi-label classification, and adapting to domain-specific schemas
Expertise in knowledge extraction and knowledge graph construction: entity recognition and linking, relation extraction, citation parsing, and building graph representations from unstructured text
Expertise in LLM-based information extraction, few-shot and multi-task learning, post-traini
About the Company
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. We serve professionals across legal, tax, accounting, compliance, government, and media. Our products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth, and transparency. Reuters, part of Thom...
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