Lead Applied Scientist, Search - NLP/GenAI

About Us

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. Sound exciting? Join us and help shape the industries that move society forward.

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 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

About You

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 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

Fähigkeiten

PythonML / AIPyTorch

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