Accelerated Science Intern, Machine Learning/Agentic AI
Required skills
- Ph.D. student
- ML/AI
- optimization
- applied mathematics
- NeurIPS
- ICML
- ICLR
- SIOPT
- Math Programming
- foundation models
Nice to have
- robotics
- vehicle design
- factory logistics
- numerical optimization
Role summary
Toyota Research Institute (TRI) is seeking a paid Machine Learning/Agentic AI Intern for a 12-week hybrid, in-office role in Cambridge. This internship offers the opportunity to apply knowledge to novel research questions and work towards publications with professional researchers. The intern will join a team building agentic AI systems for applications in robotics, vehicle design, factory logistics, and numerical optimization. Competitive compensation is provided.
At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.
This is a paid 12-week internship opportunity and is a hybrid, in-office role.
Here’s a glimpse into the Internship experience from some of our TRI interns!
Team
We are a group of researchers building agentic AI systems for a diverse range of applications, including robotics, vehicle design, factory logistics, and numerical optimization.
About the Internship
A successful candidate would have a background in both agentic AI and one of the above applications.
This is an opportunity to apply your knowledge to novel research questions and work towards a publication with professional researchers. The internship will be in our Cambridge office and includes competitive compensation befitting the challenging but fun nature of the research work at TRI.
Responsibilities
Undertake a well-scoped research project in a relevant area and bring it to publication in an appropriate venue.
Define project scope and agree on project direction with your mentor.
Qualifications
Current Ph.D. student in related fields - ML/AI, optimization, computer science, applied mathematics, or similar.
Publication background in relevant venues (e.g. NeurIPS, ICML, ICLR, SIOPT, Math Programming).
Experience with training and fine tuning foundation models with reinforcement learning.
Proficient at using agentic workflows to accelerate research explorations and experiments.
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