Vice President, Model Research & Development

About Thomson Reuters Labs

Thomson Reuters Labs is the global research and development division of Thomson Reuters. We bring together scientists, engineers, and domain experts to explore emerging technologies and develop compelling new AI products. From research and prototyping to customer success, our work bridges frontier science with practical application. Our innovations help professionals make better decisions, faster – for example, with intelligent legal assistants, automated compliance workflows, or agentic tax engines.

We are always looking for creative, highly adaptable scientists and engineers who enjoy solving hard problems that matter.

Thomson Reuters Labs builds and ships custom models for professional workflows requiring fiduciary-grade AI. The Thomson LLM model family was developed by training leading open-weight models on decades of authoritative Thomson Reuters content, with thousands of hours of subject-matter-expert input. Owning the model layer is how we earn trust in the most regulated, highest-stakes professional workflows: it lets us tune for the accuracy, citation-grounding, and jurisdictional awareness our customers require, set our own training and evaluation priorities, and build the unit economics that let AI scale profitably across products used by more than a million professionals.

This role leads that effort: an organization of scientists, research engineers, and embedded subject-matter experts responsible for advancing Thomson Reuters's LLMs. Leadership includes setting direction for the research program and product roadmap.

See the detailed technical report for more information on the latest model release:
Thomson 1.0 Technical Report on Hugging Face.

This is a hybrid role that will be based in either Zug, Switzerland or London, UK.

• Own the model-development roadmap end to end (data strategy, training, evaluation, and the release decision) for models that ship to real users across CoCounsel, Westlaw, and Practical Law.
• Set the target for an online, agentic reinforcement-learning pipeline, training with subject-matter experts in the loop, and hold the work accountable to it.
• Ensure data strategy and evaluation approach are rigorous and tied to measurable outcomes.
• Enable new product capabilities to be built directly on Thomson LLMs, with demonstrated cost and/or quality advantages over third-party alternatives.
• Build and retain a senior bench of scientists and engineers, including hiring the next wave of senior IC and leadership talent.
• Partner across product, infrastructure, and editorial/SME teams to align model priorities with what customers actually need.
• Make the model-quality, safety, and cost tradeoffs the role is accountable for, and represent that judgment to executive stakeholders.

• Program ownership. You have led a production LLM program end to end (data strategy through training, evaluation, and the release decision) for models that shipped to real users, and personally made or ratified the calls, even where execution ran through your team.
• Technical fluency. Enough command of post-training methods (SFT, preference optimization, RL), distributed training, and evaluation infrastructure to set direction, spot a training run going wrong, and be credible in the room with your own technical leads, without needing to run the experiments yourself.
• Track record. Shipped models, widely-used open-source contributions, or peer-reviewed publications at top research venues (e.g., NeurIPS, ICML, ACL, EMNLP) that speak for themselves.
• Leadership. Experience leading an organization of comparable size and shape (ideally with a manager/director layer beneath you), attracting and retaining senior ICs, and…