Research Scientist: E2E Autonomous Mobility - Honda Research Institute USA

Research Scientist: E2E Autonomous Mobility

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Research Scientist: E2E Autonomous Mobility

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  ​E2E, Autonomous Driving, Closed-loop Performance

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