Applied Science Intern

Join Thomson Reuters Labs as an Applied Science Intern and be part of a diverse global team focused on machine learning and natural language processing. In this six-month internship, you'll experience the entire model development lifecycle, collaborate with experts in various fields, and contribute to real-world challenges in legal, tax, government, and news sectors.

Participate in the full model development lifecycle, including building, testing, and delivering high-quality machine learning and NLP solutions.

About Thomson Reuters

Curiosity, intellectual openness, and a genuine passion for problem-solving through applied AI and machine learning

Responsibilities

• Collaborate with a cross-functional global team of machine learning engineers, NLP specialists, data scientists, and product teams.
• Innovate by experimenting with new approaches, learning emerging technologies, and contributing original ideas to solve complex problems.

Requirements

• Proficiency in Python and practical experience implementing solutions using deep learning, machine learning, and NLP frameworks and tools
• Experience designing, creating, and evaluating experiments related to machine learning or large language models, with a data-driven problem-solving mindset
• Understanding and hands-on experience with Natural Language Processing and/or Machine Learning methods, including exposure to Large Language Models
• Enrolled in or holding an MSc degree in Machine Learning, Natural Language Processing, Computer Science, or a closely related discipline, or currently pursuing a PhD in a relevant field
• Strong communication skills and ability to collaborate effectively as part of a diverse, distributed team
• Experience with code collaboration and versioning systems such as GitHub
• Experience working with unstructured datasets and knowledge of data cleaning, processing, and preparation techniques

Preferred

• Familiarity with cloud platforms such as Amazon AWS, Microsoft Azure, or Google Cloud
• Enthusiasm for staying current with state-of-the-art machine learning and NLP research and techniques