Research Scientist: E2E Autonomous Mobility

Job Number: P25 F22

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

• 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., Robo Racer; former F1
  
  TENTH) to full-scale vehicles.
• Contribute new research ideas and publish at top-tier venues (e.g., RAL, ICRA, IROS, CoRL, CVPR, ICCV, ECCV, NeurIPS)

• 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 Tensor Flow
• 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., Robo Racer;F1
  
  TENTH, 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

• 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

April 2027

E2E, Autonomous Driving, Closed-loop Performance