Automated Driving Advanced Development Intern, Machine Learning Research
Toyota Research Institute (TRI) is on a mission to improve the quality of human life through innovative research and development. The Automated Driving Advanced Development division is seeking Machine Learning Research Interns to contribute to the implementation and evaluation of ML-based components for autonomous driving systems.
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
Skills
- 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
- 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)
Benefits
- Medical, dental, and vision insurance
- Paid time off benefits (including holiday pay and sick time)
Company Overview
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