Are you excited about working at the forefront of applied research in an industry setting? Thomson Reuters Labs in Toronto is seeking scientists with a passion for solving problems using state-of-the-art information retrieval, natural language processing and generative AI.
What does Thomson Reuters Labs do? We experiment, we build, we deliver. We support the organization and our customers through applied research in informational retrieval, natural language processing and machine learning. We work closely with product and domain experts to identify compelling solutions at the intersection of user needs and technical feasibility. Our team is responsible for designing the next generation of tax, accounting and audit compliance software for Tax Professionals and Corporates in the US, and across the global markets. We own AI innovation for Thomson Reuters’ core tax products, including CoCounsel for Tax, Checkpoint Edge, Cloud Audit Suite, SurePrep and ONESOURCE.

ABOUT THE ROLE:

Senior Applied Scientists are experts in Machine Learning / NLP, responsible for the design and delivery of AI solutions that enhance Thomson Reuters’ products. They leverage information retrieval techniques, prompting workflows, model training and evaluation design to build and optimize solutions. Their work ensures AI technologies are effectively aligned with business objectives, driving product innovation and value.

You’re a fit for the role of Senior Applied Scientist if your background includes:

• PhD in a relevant discipline or Master’s plus a comparable level of experience
• 5+ years of hands-on experience building IR / NLP systems for commercial applications
• Experience writing production code and ensuring well-managed software delivery
• Demonstrable experience translating complex problems into successful AI applications
• Outstanding communication, problem-solving, and analysis skills
• Collaborating with Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback
• (Preferred) Publications at relevant venues such as ACL, EMNLP, NAACL, NeurIPS, ICLR, SIGIR, or KDD