Research Scientist - AI Models Science Intern: 2025

Introduction
Research Scientists are charting the future of  creating breakthroughs in quantum computing discovering how blockchain will reshape the enterprise and much more. Join a team that is dedicated to applying science to some of today’s most complex challenges whether it’s discovering a new way for doctors to help patients teaming with environmentalists to clean up our waterways or enabling retailers to personalize customer service.

Your Role and Responsibilities
This is for a 2025 summer internship with the following start dates: May – August or June – September for quarter system schools.

The MIT-IBM Watson AI lab is seeking outstanding students for a summer internship. During the internship you will work in small groups under the mentorship of experienced researchers addressing problems at the forefront of artificial intelligence . Areas of research include but not limited to new architecture s and algorithms for training fine-tuning and applying generative model s for language code and other modalities.

Job responsibilities may include identifying and defining research challenges developing prototype solutions designing experiment al setups to test hypotheses refining solutions and communicating findings through patent applications and publications in top-tier conference s.

Required Technical and Professional Expertise

• Candidates must be enrolled in a Master’s or a PhD program in computer science mathematics statistics or related disciplines.
• Strong programming skills in one or more widely used languages such as Python or Java .
• Demonstrated experience in solving analytical problems using rigorous and quantitative approaches.
• Demonstrated experience in developing training and testing deep neural network s.

Preferred Technical and Professional Expertise

• Experience with machine learning tools and frameworks such as PyTorch TensorFlow etc.
• Demonstrated experience in developing training and testing generative model s such as LLM s or multi-modal foundation model s.
• Being able to clearly and effectively communicate research ideas as demonstrated by publications and presentations at top-tier AI conference s such as NeurIPS ICLR IC ML ACL EMNLP CVPR etc.