Lead Applied Scientist, Search - NLP/GenAI in Zug

h3Lead Applied Scientist, Search - NLP/GenAI /h3pThis position is based in either Zug, Switzerland or London, UK. /ppWant to use your experience of 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 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. /ph3About the Role /h3ulliLead the design, build, test, and deployment of end-to-end AI solutions for complex document understanding tasks in the legal domain /liliDirect 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 /liliServe as the technical lead and primary point of reference, ensuring full accountability for all research deliverables /liliPartner with engineering to guarantee well-managed software delivery and reliability at scale across multiple product lines /liliEvaluate, Optimize Advance Capabilities /liliDesign comprehensive evaluation strategies for both component-level and end-to-end quality, leveraging expert annotation and synthetic data /liliApply robust training methodologies that balance performance with latency requirements /liliLead knowledge distillation initiatives to compress large models into production-ready SLMs /liliMaintain scientific and technical expertise through product deliverables, published research, and intellectual property contributions /liliInform Labs shared capabilities and research themes through novel approaches to challenging business problems /liliDrive Strategic Technical Direction /liliIndependently determine appropriate architectures for complex document understanding challenges, balancing accuracy, efficiency, and scalability /liliMake critical technical decisions on semantic chunking strategies, document classification approaches, LLM-based knowledge extraction methods, and multi-document reasoning architectures /liliProvide input to business stakeholders, mid-to-senior level leadership, and Labs leadership on long-term AI strategy /liliDevelop in-depth knowledge of TR customers and data infrastructure across multiple products to shape technical roadmaps /liliAlign, Communicate Lead Partner closely with Engineering and Product teams to translate complex legal document understanding challenges into scalable, production-ready solutions /liliEngage 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 /liliMentor and coach team members with varied ML /li /ul #J-18808-Ljbffr