Automated Driving Advanced Development Intern, Machine Learning Research

Toyota Research Institute•Los Altos, CA

3d•Hybrid

About The Position

Toyota Research Institute (TRI) is hiring a Machine Learning Research Intern for its Automated Driving Advanced Development division. The division works to connect TRI research with Toyota products, services, and needs, including a collaborative project with Woven by Toyota to research and develop a fully end-to-end learned driving stack. The intern will help bring end-to-end machine-learning models from pixels to trajectories into robust, testable, and deployable systems. The work spans machine learning, systems engineering, and real-world deployment, and includes implementing, evaluating, and integrating ML components for perception, planning, and control using simulation-based testing. The intern will collaborate with researchers, data engineers, and autonomy engineers to help models scale from prototype to production, as part of Toyota’s global AI efforts. The posting describes this as a paid 12-week internship and a hybrid, in-office role. It contains conflicting season information: the overview identifies Summer 2027, while later sections identify Summer 2026. The position is intended for someone pursuing a Ph.D. or with equivalent experience in a relevant field. The expected hourly pay at commencement is $45–$65 for California-based roles and $40–$58 for Massachusetts-based roles; a later repeated pay section lists only the California range. TRI also describes medical, dental, and vision insurance and paid time off benefits, including holiday pay and sick time.

Requirements

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 machine-learning 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.

Nice To Haves

Experience with ML engineering workflows, including data sampling and curation, preprocessing, model training, ablation studies, evaluation, deployment, and inference optimization.
Understanding of debugging and profiling on the NVIDIA CUDA stack.
Hands-on experience with metrics dashboards, experiment tracking, and MLOps tools such as Weights & Biases, MLflow, or Metaflow.
Hands-on experience with robotics or real-world sensor data, such as video, lidar, IMU, or radar.
Experience with state-of-the-art architectures for object detection and 3D perception.
Familiarity with foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures, or large-scale distributed training.
Experience with ROS, simulation frameworks such as CARLA or Nvidia DriveSim, or vehicle interfaces.
Experience with robot motion-planning techniques such as trajectory optimization, sampling-based planning, or model predictive control, or with automated-driving domains such as perception, prediction, mapping, localization, planning, or simulation.
Relevant open-source contributions or technical project write-ups; applicants are asked to include links with their application.

Responsibilities

Conduct research on generative modeling capabilities for end-to-end planning from vision in automated driving.
Implement scalable end-to-end architectures that process raw sensor data and generate vehicle trajectories, addressing long-tail driving scenarios with low data coverage.
Prototype, validate, and iterate on model architectures using imitation learning and large-scale data, with the aim of robust performance across diverse scenarios.
Perform closed-loop evaluations in sensor simulations and real-world testing environments.
Explore multimodal and language-conditioned models, using external data sources and transfer learning to improve the applicability and generalization of end-to-end policies.
Implement, evaluate, and integrate ML-based components for perception, planning, and control, including simulation-based testing.
Collaborate with researchers, data engineers, and autonomy engineers to support scaling models from prototype to production.