Human Interactive Driving Research Intern, World Models
About The Position
Toyota Research Institute is hiring a paid, 12-week Ph.D. Research Intern for a hybrid, in-office role on the Extreme Performance Intelligent Control (EPIC) team within the Human Interactive Driving division. The multidisciplinary team is developing a world foundation model for driving, using large-scale real-world and simulated data to build transferable representations of driving knowledge. Its work addresses multi-agent interaction, causal reasoning, and embodied intelligence, and involves collaboration with engineers to deploy models in simulation and on hardware. The intern will conduct original research at the intersection of machine learning and autonomous driving, advancing world modeling in areas such as representation learning, action conditioning, and evaluation. The role includes prototyping and evaluating ideas in closed-loop simulations, working with real-world data, and contributing to research publications with TRI scientists.
Requirements
- Currently pursuing a Ph.D. in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field.
- Strong background in machine learning, particularly 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 and expertise with modern deep learning frameworks such as PyTorch, JAX, or TensorFlow.
- Prior publication(s) in top-tier AI/ML conferences.
Nice To Haves
- Experience with probabilistic and generative modeling.
- Experience with autonomous driving datasets.
- Experience with open-source foundation models.
- Experience with Amazon Web Services, such as S3, EC2, or SageMaker.
- Links to relevant open-source contributions or technical project write-ups are requested with the application.
Responsibilities
- Conduct original research in one or more areas: world modeling, multi-agent interaction, reinforcement learning, perception, or sim-to-real transfer.
- Collaborate with full-time researchers on the design, training, and evaluation of learning-based driving systems.
- Develop and evaluate prototypes in closed-loop simulation environments and, time permitting, on high-performance autonomous driving hardware.
- Present research findings through internal talks and work toward a top-tier academic publication.
- Integrate and work with large-scale open-source and internal datasets.
Benefits
- Medical insurance
- Dental insurance
- Vision insurance
- Paid time off, including holiday pay and sick time
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
