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Kadence
Lead Applied ScientistKadence • Charing Cross, Greater London, United Kingdom
Lead Applied Scientist

Lead Applied Scientist

Kadence • Charing Cross, Greater London, United Kingdom
5 days ago
Job type
  • Full-time
Job description

Job Description

Lead Applied Scientist, Search - NLP/GenAI



Please read the information in this job post thoroughly to understand exactly what is expected of potential candidates.

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



Want 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. 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:




  • 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



You're a fit for the role of Lead Applied Scientist if you have:



  • 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 xwwtmva 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 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



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Lead Applied Scientist • Charing Cross, Greater London, United Kingdom

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