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! 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 Summer 2027 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.   The Team   The Automated Driving Advanced Development division at TRI will focus on enabling innovation and transformation at Toyota by building a bridge between TRI research and Toyota products, services, and needs. We achieve this through partnership, collaboration, and shared commitment. This new division is leading a new cross-organizational project between TRI and Woven by Toyota to conduct research and develop a fully end-to-end learned driving stack. This cross-org collaborative project is harmonious with TRI’s robotics divisions' efforts in Diffusion Policy and Large Behavior Models.   The Internship   We are looking for Machine Learning Research Interns to join our autonomy team and help bring end-to-end ML models ( pixels to trajectories ) into robust, testable, and deployable systems. This role is ideal for those who thrive at the intersection of machine learning, systems engineering, and real-world deployment. This internship opportunity is a paid 12-week internship for Summer 2027. Please note that this internship will be a hybrid in-office role.   You’ll contribute to the implementation, evaluation, and integration of ML-based components for perception, planning, and control; with simulation-based testing. You’ll work closely with researchers, data engineers, and autonomy engineers to ensure models scale from prototype to production. This work is part of Toyota’s global AI efforts to build a more coordinated global approach across Toyota entities.   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).