Introduction
Are you excited about the latest advances in foundation models? Are you interested in advancing efficiency of large language models, publishing your work in the most prestigious AI conferences in the world, and making your code open source? How about working closely with top-notch MIT faculty, students, and IBM scientists in a flexible and fun environment? Are you passionate about opportunities to bridge your scientific work to real-world impact? If you answered yes to these questions, then you should apply to our summer internship position with background in natural language processing to join our team.
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.
Want more jobs like this?
Get Science and Engineering jobs delivered to your inbox every week.
The candidate will be responsible for conducting cutting-edge research on natural language processing, developing prototype solutions to real-world problems, and publishing papers in top-quality conferences. The ideal candidate will be hands on with distributed programming skills, stays on top of AI research and will be responsible for wide spectrum of tasks from ideation, running ablation experiments to prove out ideas, distributed model training, evaluation on various use cases and will enable better collaboration with other IBM teams for areas that need joint attention.
Required Technical and Professional Expertise
- Applicants should be PhD & MS students.
- Ability to quickly prototype ideas and use creative approaches for developing large language models.
- Developing novel architectures for language models and implement these models using open-source libraries.
- Making language models more interpretable, explainable, and transparent.
- Systematically evaluating language models on standardized academic and enterprise benchmarks.
Preferred Technical and Professional Expertise
- Publication in top AI conferences
- Experience with distributed training and inference of large language models