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Research Scientist:

E2E Autonomous Mobility Honda Research Institute USA Mountain View, CA Job Details 22 hours ago Qualifications AI models Engineering development testing Publishing papers in peer-reviewed journals AI platforms (beyond public GPTs) Doctor of Philosophy Model deployment Developing large-scale AI models Model training Machine learning (ML) fundamentals Model evaluation

Full Job Description Job Number:

P25F22 Honda Research Institute USA (HRI-US) is seeking a highly motivated Research Scientist to contribute end-to-end pipeline and learning technologies for autonomous mobility, spanning perception, scene understanding, planning and control. A successful candidate will have experience with one or more of the following: end-to-end driving model/stack, closed-loop performance improvement, end-to-end hardware experiments on real vehicles, vision-language-action models, world models, imitation or reinforcement learning for driving. The ideal candidate combines strong research capabilities with practical experience deploying models in real-world systems. Mountain View, CA Key Responsibilities Research and develop end-to-end learning pipelines for autonomous mobility, spanning perception, scene understanding, and driving policy. Design and train end-to-end driving models, including vision-language-action models, foundation models, world models, and imitation or reinforcement learning-based policies. Improve closed-loop performance through simulation, data-driven evaluation, and iterative model refinement with simulation and hardware. Lead end-to-end hardware experiments on real vehicle platforms, from small-scale cars (e.g., RoboRacer; former F1TENTH) to full-scale vehicles. Contribute new research ideas and publish at top-tier venues (e.g., RAL, ICRA, IROS, CoRL, CVPR, ICCV, ECCV, NeurIPS) Minimum Qualifications Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related field 2+ years of hands-on experience in machine learning / deep learning with PyTorch or TensorFlow Research experience in one or more of: end-to-end driving model/stack, end-to-end training of differentiable modules, vision-language-action models, world models, imitation or reinforcement learning for driving, or closed-loop performance improvement Hands-on experience deploying and testing learned models on real vehicle hardware, including small-scale autonomous cars (e.g., RoboRacer;F1TENTH, MuSHR, Duckietown) and/or full-scale vehicles Strong programming skills in Python and/or C++ Experience building end-to-end training and evaluation pipelines for perception or driving models Strong publication record in robotics, controls, computer vision, or machine learning Bonus Qualifications Experience running closed-loop experiments on full-scale autonomous vehicles, including safety driver protocols and on-vehicle data logging Experience with ROS/ROS2, embedded compute (e.g., NVIDIA Jetson, DRIVE), and vehicle sensor stacks (camera, LiDAR, radar, IMU) Experience with model optimization for real-time on-vehicle inference (quantization, distillation, TensorRT) Experience with closed-loop simulation (e.g., CARLA, Waymax, NVIDIA Alpasim, Isaac sim) and sim-to-real transfer Experience with LLMs, VLMs, or foundation models for driving or scene understanding Experience with large-scale distributed training and multimodal datasets Desired Start Date April 2027 Position Keywords E2

E, Autonomous Driving, Closed-loop Performance Warning:

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