Toyota Ventures is Toyota’s first standalone early-stage venture capital firm.
About the role
We are seeking a Robotics Research Intern to investigate agentic approaches to robotic manipulation. You will work closely with researchers and engineers to develop systems that connect foundation-model reasoning with physical robot capabilities. Depending on your background and research interests, your project may focus on spatial reasoning, robotic skill orchestration, execution monitoring, failure recovery, or systematic evaluation of agentic manipulation systems. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Currently pursuing a Master's or PhD degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field
- Strong programming skills in C++ and/or Python and experience developing, testing, or debugging technical software
- Solid understanding of robotics fundamentals, including one or more of robot kinematics, dynamics, motion planning, perception, or control
- Experience with robotics-related projects through research, coursework, or personal projects, using physical hardware and/or simulation
- Ability to formulate technical problems, design experiments, and systematically analyze results
- Interest in robotic manipulation, autonomous systems, and connecting high-level reasoning with physical robot capabilities
More about this role
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!
TRI's Robotics Division develops technologies that enable robots to operate effectively in complex, real-world environments. The project explores how foundation-model agents can understand manipulation tasks, reason about physical environments, and orchestrate reusable robotic capabilities to accomplish diverse objectives. Our research combines vision-language models, robotic perception, motion planning, manipulation, and robot learning. We investigate these questions through hands-on experimentation on physical robotic platforms.
Recent advances in foundation models have significantly improved AI systems' ability to understand visual scenes, reason about tasks, and plan sequences of actions. However,...
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