Toyota Ventures is Toyota’s first standalone early-stage venture capital firm.
About the role
We are seeking a motivated intern to join our team, focusing on cutting-edge research combining machine learning with physical system knowledge and control theory to develop reliable controllers for autonomous vehicles. Potential research areas include designing learning-based control methods that enforce forward invariance, stability, and constraint satisfaction; and incorporating vehicle dynamics and other physical knowledge into learning and control amid challenging driving scenarios. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Ph.D. or M.S. student in relevant fields - ML/AI, robotics, computer science, mechanical engineering, aerospace engineering, or similar
- Proficient in scientific Python, Unix, and robotic frameworks (e.g. ROS2.0)
- Knowledge of machine learning, nonlinear system control, and optimal control. Vehicle dynamics knowledge is a plus
- Experience working on joint scientific and engineering projects
- Familiarity with or interest in racing or high-performance driving is a plus
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!
The Team
Within the Extreme Performance Intelligent Control team, we are developing techniques to advance vehicle safety and performance capabilities using tools from machine learning, optimization, and control theory. We pursue a hybrid approach to high-performance driving that unites physics-based optimal control with data-driven learning. By embedding vehicle dynamics and optimization structure into learned controllers, we aim to build systems that adapt to uncertainty and match or exceed expert drivers while retaining the interpretability and guarantees of classical methods.
The Internship
We are seeking a motivated intern to join our team, focusing on cutting-edge research combining machine learning...
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