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 ground-breaking shift in mobility, we’ve built a world-class team in Robotics, Large Behavior Models, Energy & Materials, Human-Centered AI, and Human-Interactive Driving.
This is a summer 2026 paid 12-week internship opportunity. Please note that this internship will be an in-office role in Cambridge, MA.
The Team
Our goal is to revolutionize the field of robotic manipulation, enabling long-horizon dexterous behaviors to be efficiently instructed, learned, and improved over time in diverse, real-world environments.
This team has deep cross-functional expertise across simulation, perception, controls, and machine learning, and we measure our success in terms of fundamental capabilities development, as well as research impact via open-source software and publications. Our north star is fundamental technological advancement in building robots that can flexibly perform a wide variety of tasks in diverse environments with minimal human supervision. Join us and let’s make general-purpose robots a reality.
The Internship
We are looking for a Mechanical Engineering Intern to contribute to our robot hardware development and testing group. The intern who joins our team will be responsible for the design, fabrication, ordering, and assembly of robotic systems and test spaces, including both structural and electromechanical components. Responsibilities include working closely across subject areas with team members to define project requirements and support project needs (e.g. rapid iteration on robot component designs, maintenance, and repair of robotic systems), and ensure the robotic lab remains safe, clean, and effective. The intern will also plan, execute, document, and communicate testing activities, providing clear and accurate reporting of test conditions and results. Occasional travel may be required, and additional responsibilities may be assigned as needed.