T
Not Specified Permanent

Ann Arbor, Michigan · USA job

Senior Applied Scientist

Thomson Reuters

Ann Arbor, Michigan

Job description

Overview

In this role you will lead end-to-end applied research projects that translate business needs into scalable AI capabilities. You will mentor scientists and influence technical direction within TR Labs, driving product innovation in legal intelligence, tax automation, or regulatory compliance. You will tackle complex problems at the intersection of research and real-world impact, collaborating with cross-functional teams. This is a chance to shape next-generation AI solutions that support professionals and uphold Thomson Reuters' mission.

Compensation / Benefits
  • hybrid work model
  • flexible vacation
  • two company-wide mental health days
  • tuition reimbursement
  • employee incentive programs
  • work-life balance initiatives
Responsibilities
  • Lead end-to-end applied research projects from problem framing to delivery
  • Translate business challenges into technical solutions and assess feasibility
  • Deliver scalable AI capabilities in legal, tax, or regulatory domains
  • Mentor scientists and influence technical direction within the team
  • Grow impact by expanding adoption and cross-functional collaboration
Key requirements
  • PhD or Master's in Computer Science, ML, or related field
  • 5+ years hands-on experience building and deploying production IR, NLP, ML, or GenAI systems
  • Experience leading end-to-end AI project delivery in cross-functional environments
  • Outstanding technical judgment and evaluation expertise
  • Excellent communication skills with ability to influence technical direction
  • Experience developing novel methods for domains such as medical, tax, or law
  • Expertise in RAG architectures, agentic frameworks, tool-using systems, or orchestration patterns
  • Publications at ACL/EMNLP/NAACL/NeurIPS/ICLR/SIGIR/KDD or related venues
  • Strong communication and influencing ability
  • Cross-functional collaboration
  • Mentorship and leadership
  • Production IR/NLP/ML/GenAI system development
  • RAG architectures and agentic frameworks
  • Tool-using systems and orchestration patterns

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