Thomson Reuters is seeking an Applied Scientist to contribute to their AI Research & Development arm, TR Labs, focusing on developing machine learning and AI solutions to address business challenges in legal, tax, and regulatory domains. The role requires strong programming and communication skills, relevant academic qualifications, and experience with modern AI/ML techniques
Job Summary
The role involves developing and implementing machine learning and AI solutions to address business problems across Thomson Reuters' legal, tax, and regulatory domains.
Candidates must have strong programming skills in Python and familiarity with modern ML frameworks like PyTorch or JAX.
Thomson Reuters offers a comprehensive benefits package including flexible vacation, two company-wide Mental Health Days off, and tuition reimbursement.
Matching Summary
Match Score: 85
Thomson Reuters is seeking an Applied Scientist to contribute to their AI Research & Development arm, TR Labs, focusing on developing machine learning and AI solutions to address business challenges in legal, tax, and regulatory domains. The role requires strong programming and communication skills, relevant academic qualifications, and experience with modern AI/ML techniques.
Salary
Base: $117,700 - $218,600 USD (US locations); Base: $80,000 - $100,000 CAD (Ontario); Bonus: Eligible for Annual Bonus based on performance; Benefits: Comprehensive health, dental, vision, 401k match, mental health days, tuition reimbursement
Skills & Requirements
Must-have
PhD or Master's degree in Computer Science
Python programming skills
Experience with PyTorch or JAX frameworks
Knowledge of transformer architectures
Experience with LLM evaluation frameworks
Nice-to-have
Publications in NeurIPS or ACL venues
MLOps practices experience
Strong communication across functions
Cloud development environment familiarity
Agentic systems expertise
Key Requirements
1-2 years of hands-on experience building systems
PhD or Master's degree required
Experience with retrieval-augmented generation (RAG)