Automated Driving Advanced Development Intern, Machine Learning Research
About the positionResponsibilities• 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. 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 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. Nice-to-haves• 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• TRI offers a generous benefits package including medical, dental, and vision insurance, and paid time off benefits (including holiday pay and sick time). Apply tot his job