Toyota Ventures is Toyota’s first standalone early-stage venture capital firm.
About the role
You will be joining the Extreme Performance Intelligent Control (EPIC) team in the Human Interactive Driving division, a multidisciplinary research team focused on developing a world foundation model for driving—a unified, transferable representation of driving knowledge built from large-scale real-world and simulated data. Together, we are tackling the complex challenges of multi-agent interaction, causal reasoning, and embodied intelligence in highly dynamic, real-world driving environments. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Currently pursuing a Ph.D. in Computer Science, Robotics, Machine Learning, Electrical Engineering, or related fields
- Strong background in machine learning, particularly in areas such as deep learning, generative models, reinforcement learning, or probabilistic modeling
- Hands-on experience training and evaluating world models, model-based reinforcement learning, 3D reconstruction or sensor fusion, and large-scale simulation for robotics or autonomous systems
- Proficient coding skills with expertise in modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow, etc.)
- Prior publication(s) in top-tier AI/ML conferences
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!
You will be joining the Extreme Performance Intelligent Control (EPIC) team in the Human Interactive Driving division, a multidisciplinary research team focused on developing a world foundation model for driving—a unified, transferable representation of driving knowledge built from large-scale real-world and simulated data. Together, we are tackling the complex challenges of multi-agent interaction, causal reasoning, and embodied intelligence in highly dynamic, real-world driving environments. Our approach is inspired by cutting-edge generative modeling techniques and powered by scalable infrastructure and open-source data, and we work closely with engineers to deploy our models in simulation and on...
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