ML/NLP Scientist Intern: Hands-on Research & Impact

Thomson Reuters; Eagan (MN); On-site; USD 34,000 - 48,000; Full time

Benefits offered by this job

Learning & Development

High-Impact Problems

Competitive Compensation

Job summary

Thomson Reuters Labs is seeking an Applied Scientist Intern with a passion for problem-solving using state-of-the-art Machine Learning and NLP techniques. The internship runs January 11, 2027 to July 12, 2027 and offers exposure to a global, interdisciplinary team.

You will contribute to model development, training, evaluation, and collaboration across Thomson Reuters’ diverse product areas, including Reuters News, Legal, and Tax & Accounting teams.

Qualifications

• Currently enrolled in a Master’s or PhD program with research experience.
• Experience with NLP/ML methods, including Large Language Models.
• Experience with model training, fine tuning, evaluation of ML/LLM experiments.
• Proficiency in Python and cloud platforms (preferably AWS).
• Experience with ML/NLP libraries such as PyTorch, TensorFlow, Scikit-learn, spaCy, LangChain, and HuggingFace.
• Comfort with unstructured data and data cleaning techniques.
• Strong communication and collaboration skills.

Responsibilities

• Experiment and Develop: participate in model development lifecycle, build, test, and deliver high-quality solutions.
• Collaborate: work within a cross-functional team across the globe.
• Innovate: explore new approaches and technologies to solve real-world challenges.

Skills

Python

NLP/ML concepts

Large Language Models

AWS

PyTorch

TensorFlow

Scikit-learn

spaCy

LangChain

HuggingFace

Data cleaning

Communication skills

Education

Master's or PhD candidate

Tools

Jupyter

Git

Docker

Job description

Thomson Reuters Labs is seeking an Applied Scientist Intern with a passion for problem-solving using state-of-the-art Machine Learning and NLP techniques. The internship runs January 11, 2027 to July 12, 2027 and offers exposure to a global, interdisciplinary team.

You will contribute to model development, training, evaluation, and collaboration across Thomson Reuters’ diverse product areas, including Reuters News, Legal, and Tax & Accounting teams.