Automated Driving Advanced Development Intern, Machine Learning Research

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!

Responsibilities

• Conduct ambitious research to advance the state-of-the-art in using new capabilities in generative modelling for end-to-end planning from vision in automated driving.
• Implement scalable end-to-end architectures that process raw sensor data to generate vehicle trajectories, addressing the challenges of long-tail driving scenarios with low data coverage.
• Prototype, validate, and iterate on model architectures using imitation learning, and large-scale data, ensuring robust performance across diverse scenarios.
• Perform closed-loop evaluations in sensor simulations and real-world testing environments.
• Explore multi-modal and language-conditioned models to broaden the applicability of end-to-end policies, leveraging external data sources and transfer learning to enhance generalization.

Qualifications

• Currently pursuing a Ph.D. or equivalent experience in Computer Science, Robotics, Engineering, or a related field.
• Proficiency in Python for implementing and evaluating research ideas.
• Experience with ML frameworks such as PyTorch.
• Understanding of version control, testing, and software engineering fundamentals.
• Passion for collaborative engineering and building reliable ML systems that support real-world autonomy.

Bonus Qualifications

• Experience in ML engineering workflows: data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, inference optimization.
• Understanding of debugging and profiling on NVIDIA CUDA stack.
• Hands-on experience with metrics dashboards, experiment tracking, and ML ops tooling (e.g., Weights & Biases, MLflow, Metaflow).
• Hands-on experience working with robotics or real-world sensor data (e.g., video, lidar, IMU, or radar).
• Experience in state-of-the-art architectures for object detection and 3D perception.
• Familiarity with foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures, large-scale distributed training.
• Experience working with ROS, simulation frameworks (e.g., CARLA, Nvidia DriveSim), or vehicle interfaces.
• Experience with robot motion planning techniques like trajectory optimization, sampling-based planning, or model predictive control, or experience with automated driving domains (e.g., perception, prediction, mapping, localization, planning, simulation).