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Thomson Reuters · 18 hours ago

Lead Applied Scientist, NLP/GenAI

Eagan, MN

Full-time

Hybrid

Senior Level, Lead/Staff

$147K/yr - $273K/yr

7+ years exp

Thomson Reuters is a global provider of trusted content and technology solutions for professionals across various sectors. They are seeking a Lead Applied Scientist to innovate and deliver AI solutions for complex document understanding tasks in the legal domain, ensuring the development of systems that enhance the capabilities of their legal AI platform.

AdviceAnalyticsFinancial ServicesManagement ConsultingProfessional ServicesRisk ManagementSoftware

H1B Sponsor Likely

Hiring Manager

Lukas Pasvianskas

Responsibilities

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

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

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

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

Qualification

Document understanding systemsDeep learning frameworksKnowledge graph constructionNLP methodsSemantic chunkingInformation extractionSynthetic data generationModel optimizationProgramming skillsStakeholder engagementTechnical leadershipMentoringCommunication skills

Required

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

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-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 knowledge distillation, 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

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

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

Thomson Reuters

Glassdoor3.0

Thomson Reuters delivers critical information from the financial, legal, accounting, intellectual property, science, and media markets.

Founded in 2008

Toronto, Ontario, CAN

10001+ employees

http://thomsonreuters.com

H1B Sponsorship

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