Thomson Reuters
Lead Research Engineer
$127K–$273K
United States of America, Eagan, Minnesota +6 · Hybrid · Full-time · Posted Oct 2, 2026
- Location
- United States of America, Eagan, Minnesota
Canada, Toronto, Ontario
United States of America, New York, New York
United States of America, Ann Arbor, Michigan
United Kingdom, London
Switzerland, Zug, Zug
United States of America, Frisco, Texas - Experience
- At least 8 years of software engineering experience; ideally in machine learning and natural language processing.
- Education
- Bachelor of Science degree in computer science or a related field.
About the job
Leads engineering practices and teams while building and productionizing scalable machine-learning systems and data pipelines for Thomson Reuters products.
What you'll do
- Set engineering standards and provide technical leadership, mentorship, and guidance across workstreams.
- Build, test, and deliver scalable ML systems and large-scale data-processing pipelines throughout the software lifecycle.
- Collaborate with researchers and cross-functional teams to evaluate, prototype, and productionize research concepts.
What they ask for
- At least 8 years of software engineering experience, ideally in machine learning and natural language processing.
- Bachelor of Science degree in computer science or a related field.
- Experience leading technical workstreams and mentoring engineers.
- Deep understanding of Python development; experience building and integrating ML solutions in production, including ModelOps and MLOps principles.
Nice to have
- Familiarity with Python data science libraries such as NumPy, SciPy, Pandas, Dask, spaCy, NLTK, scikit-learn, and PyTorch.
- Familiarity with AWS or Azure, NLP tasks, probabilistic models, and other programming languages such as Java, TypeScript, or JavaScript.
Pay
$146,800 USD - $272,600 USD (base salary); $127,400 USD - $236,600 USD (base salary); $140,000 CAD - $175,000 CAD (base salary)
Tools
Python, NumPy, SciPy, Pandas, Dask, spaCy, NLTK, PyTorch
AI focus
Develops scalable machine-learning systems and data pipelines, and collaborates with researchers to evaluate, prototype, and productionize ML and NLP capabilities.
Model-written summary · the original posting is authoritative