Senior Applied Scientist, Search - NLP/GenAI

Senior Applied Scientist, Search - NLP/Gen AI

This position is based in either Zug, Switzerland or London, UK.

Want to use your experience building search‑led AI solutions to enhance our leading products in the tax, legal and professional services industries?

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 such as Westlaw, Practical Law, and Co Counsel.

In this role you will build state‑of‑the‑art semantic chunking, document enrichment, and knowledge graph construction systems that serve as the cognitive foundation for multiple product teams working across authoritative legal, tax and accounting content and diverse customer data.

About the Role

• Innovate & Deliver: Design, build, test and deploy end‑to‑end AI solutions for complex document understanding tasks in the legal domain.

Develop advanced models for semantic chunking of lengthy, non‑uniformly structured documents with adjustable granularity levels for different use cases.

Build document enrichment systems that classify documents according to legal and customer‑defined taxonomies and extract rich metadata.

Create LLM‑based knowledge graph construction pipelines that extract and link heterogeneous legal knowledge, including citations, entities and legal concepts across diverse content.

Develop scalable synthetic data generation systems to support model training, simulate complex legal research queries and generate hallucination‑free answers.

Work with engineering to ensure well‑managed software delivery and reliability at scale.

• Evaluate & Optimize: Develop comprehensive data and evaluation strategies for component‑level and end‑to‑end quality, leveraging expert human annotation and synthetic data generation.

Apply robust training and evaluation methodologies that balance model performance with latency requirements, particularly for SLM‑based solutions.

Use knowledge‑distillation techniques to compress large models into efficient SLMs suitable for production deployment.

• Drive
• Technical

Decisions: Independently determine appropriate architectures for challenging document understanding problems, including semantic chunking strategies that handle diverse formats, preserve document structure and adapt to different granularity needs; document classification approaches that generalize across legal taxonomies; LLM‑based knowledge extraction methods that handle citation recognition errors and contextual references; multi‑document reasoning architectures for generating synthetic multi‑hop queries that reflect complex legal research patterns.

Balance accuracy, efficiency and scalability while addressing real‑world challenges.

• Align & Communicate: Partner closely with Engineering and Product teams to translate complex document understanding challenges into scalable, production‑ready solutions.

Engage stakeholders across multiple product lines to deeply understand use‑case requirements, shaping objectives aligned with diverse business needs, including next‑generation search and deep legal research.

• Advance the

Field: Maintain scientific and technical expertise in one or more relevant areas through product deliverables, published research at top venues (ACL, EMNLP, ICLR, Neur IPS, SIGIR, KDD), and intellectual property.

About You

• Ph D in Computer Science, AI, NLP, or a related field, or a Master’s with equivalent research/industry experience.
• 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.
• Professional experience scaling yourself and leading through others in an applied research setting.
• Strong programming skills (Python) and experience with modern deep‑learning frameworks (Py Torch, Hugging Face Transformers, Deep Speed).
• Publications at relevant venues such as ACL, EMNLP, ICLR, Neur IPS, SIGIR, KDD.
• Technical Qualifications
• Deep understanding of document understanding fundamentals: layout analysis, semantic chunking beyond fixed‑size or paragraph‑based methods, document classification handling hierarchical taxonomies, 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 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.

What’s in it For You?

• Hybrid Work Model: Flexible hybrid working environment (2‑3 days a week in the office depending on the role) with a seamless digital and physical connection.
• Flexibility & Work‑Life Balance: Work‑from‑anywhere for up to 8 weeks per year and supportive policies to manage personal and professional responsibilities.
• Career Development and Growth: Continuous learning and skill development, Grow My Way programming and a skills‑first approach to help you grow, lead and thrive.
• Industry
• Competitive

Benefits: Flexible vacation, two company‑wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs and resources for mental, physical and financial wellbeing.

• Culture: Inclusion, belonging, flexibility, work‑life balance and values such as Obsess over our Customers, Compete to Win, Challenge Your Thinking, Act Fast / Learn Fast and Stronger Together.
• Social Impact: Two paid volunteer days off annually and opportunities to participate in pro‑bono consulting projects and ESG initiatives.
• Making a Real‑World Impact: Helping customers pursue justice, truth and transparency, upholding the rule of law, and providing trusted, unbiased information worldwide.
• DISCLAIMER

The above information in this description has been designed to indicate the general nature and level of work performed by employees within this classification.

It is not designed to be interpreted as a comprehensive inventory of all duties, responsibilities, and qualifications required of employees assigned to this job.

Equal Employment Opportunity

Thomson Reuters is a global equal‑employment‑opportunity employer.

All qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, age, disability, pregnancy, veteran status or any other protected classification in the locations where the position is offered.

We also provide reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law.

#J-18808-Ljbffr