Lead Applied Scientist, Search – NLP/GenAI

Overview

In this role you will lead end-to-end AI work for advanced document understanding in the legal domain, shaping production-grade capabilities used across search, extraction, and reasoning in top Thomson Reuters products. You will guide large-scale projects on semantic chunking, document enrichment, and knowledge-graph pipelines, collaborating with cross-functional teams to deliver impactful AI. Your work will influence how legal professionals research and analyze complex documents at scale, while advancing state-of-the-art techniques. This role offers strategic technical direction and opportunities to publish and contribute IP in a fast-evolving field.

Pay / Benefits

• flexible hybrid work model
• flexible vacation
• Mental Health Days
• Headspace app access
• retirement savings
• tuition reimbursement

Responsibilities

• Lead design, build, test, and deployment of end-to-end AI solutions for document understanding in the legal domain
• Direct execution of large-scale projects: semantic chunking, document enrichment with taxonomies, knowledge graph pipelines, and synthetic data generation
• Serve as the technical lead and accountable for research deliverables
• Collaborate with engineering to ensure scalable, reliable software delivery across product lines
• Design evaluation strategies for component and end-to-end quality using expert annotation and synthetic data
• Lead model distillation to produce production-ready SLMs and optimize performance vs latency
• Maintain expertise through product deliverables, publications, and IP
• Inform Labs capabilities and research themes with novel approaches to business problems
• Develop deep knowledge of customer data infrastructure to shape technical roadmaps
• Partner with Engineering and Product to translate challenges into scalable solutions
• Mentor and coach team members across ML/NLP capabilities

Key requirements

• PhD in Computer Science, AI, NLP, or related field, or Master’s degree with equivalent research/industry experience
• Hands-on experience building and deploying document understanding, information extraction, or knowledge graph systems using deep learning and NLP methods
• Ability to translate complex document problems into innovative AI applications balancing accuracy and efficiency
• Proven technical leadership, mentoring, and influence in applied research
• Strong programming skills (Python) and experience with PyTorch, Hugging Face Transformers, DeepSpeed
• Publications at ACL, EMNLP, ICLR, NeurIPS, SIGIR, or KDD

• technical leadership
• mentoring
• cross-functional collaboration
• Python
• PyTorch
• Hugging Face Transformers

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