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About the Role

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, document enrichment systems, LLM-based knowledge graph construction pipelines, 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.

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
Direct the execution of document enrichment systems with legal and customer-defined taxonomies
Direct the execution of LLM-based knowledge graph construction pipelines that extract and link heterogeneous legal knowledge
Direct the execution of 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

Requirements

PhD in Computer Science, AI, NLP, or a related field, or a Master's degree with equivalent research/industry experience
Demonstrable 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
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

Skills

Python
PyTorch
Hugging Face Transformers
DeepSpeed
NLP
GenAI
Deep Learning
LLMs
Document Understanding
Information Extraction
Knowledge Graph Construction
Semantic Chunking
Document Enrichment
Knowledge Distillation
Synthetic Data Generation
ML

Location

Zug, Switzerland
London, UK

Work Type

Hybrid

Experience Level

Lead

Education Level

PhD in Computer Science, AI, NLP, or a related field
Master's degree with equivalent research/industry experience

Benefits

Flexible vacation
Two company-wide Mental Health Days off
Access to the Headspace app
Retirement savings
Tuition reimbursement
Employee incentive programs
Resources for mental, physical, and financial wellbeing
Work from anywhere for up to 8 weeks per year

About the Company

Thomson Reuters 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 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.
Join us and help shape the industries that move society forward.

Equal Opportunity

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, including 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.