Lead Applied Scientist, NLP/GenAI

Join to apply for the Lead Applied Scientist, NLP/GenAI role at Thomson Reuters. 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 beyond fixed-size or paragraph-based methods, hierarchical taxonomy classification, imbalanced multi-label classification, and domain-specific schema adaptation. 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-training, and knowledge distillation. Solid understanding of synthetic data generation techniques for NLP, including query-answer generation with verification and scalable data augmentation for training specialized models. Solid understanding of efficiency optimization including model compression and designing SLM-based solutions that balance performance with computational constraints. Solid understanding of DL/ML approaches used for NLP tasks. Experience designing annotation workflows, creating high-quality labeled datasets with clear guidelines, and developing evaluation frameworks for document understanding tasks. Preferred Qualifications Prior work on legal document understanding, legal information extraction, knowledge representation including legal citations and legal domain concepts, or legal AI applications. Prior work handling complex document structures common in legal documents: non-uniform formatting, nested hierarchies, cross-references, and embedded elements. Experience building systems that perform analysis, question answering, or retrieval across large document collections. Experience with knowledge graph frameworks and methodologies for legal or enterprise applications. Understanding of RAG and agentic workflows for enterprise knowledge. Experience working with AzureML or AWS SageMaker. What’s in it For You? Flexibility & Work-Life Balance: Flex My Way policies, work from anywhere for up to 8 weeks per year. Career Development and Growth: Grow My Way programming and skills-first approach. Industry Competitive Benefits: comprehensive benefit plans including flexible vacation, mental health days, Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and wellness resources. Culture: inclusion, belonging, flexibility, work-life balance, values of customer obsession, competitive spirit, challenging thinking, fast learning, and teamwork. Social Impact: Social Impact Institute, volunteer days, ESG initiatives. Real-World Impact: Supporting justice, truth, transparency, and unbiased information worldwide. In the United States, Thomson Reuters offers a comprehensive benefits package to our employees. Our benefit package includes market competitive health, dental, vision, disability, and life insurance programs, as well as a competitive 401(k) plan with company match. In addition, Thomson Reuters offers market leading work life benefits with competitive vacation, sick and paid time off, paid holidays (including two company mental health days off), parental leave, sabbatical leave. These benefits meet or exceed the requirements of paid time off in accordance with any applicable state or municipal laws. Thomson Reuters complies with local laws that require upfront disclosure of the expected pay range for a position. The base compensation range varies across locations. For any eligible US locations, unless otherwise noted, the base compensation range for this role is $147,000 - $273,000. This role may also be eligible for an annual bonus based on a combination of enterprise and individual performance. Base pay is positioned within the range based on several factors including an individual’s knowledge, skills and experience with consideration given to internal equity. Base pay is one part of a comprehensive Total Reward program which also includes flexible and supportive benefits and other wellbeing programs. This job posting will close 12/17/2025. About Us Thomson Reuters informs the way forward by bringing together 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 Thomson Reuters, is a world leading provider of trusted journalism and news. We are powered by the talents of 26,000 employees across more than 70 countries, where everyone has a chance to contribute and grow professionally in flexible work environments. At a time when objectivity, accuracy, fairness, and transparency are under attack, we consider it our duty to pursue them. Sound exciting? Join us and help shape the industries that move society forward. As a global business, we rely on the unique backgrounds, perspectives, and experiences of all employees to deliver on our business goals. To ensure we can do that, we seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. Thomson Reuters is proud to be an Equal Employment Opportunity Employer providing a drug-free workplace. We also make reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law. More information on requesting an accommodation is available. Learn more on how to protect yourself from fraudulent job postings here. More information about Thomson Reuters can be found on thomsonreuters.com. #J-18808-Ljbffr