Research Intern: Closed-Loop E2E Driving - Honda Research Institute USA

Research Intern: Closed-Loop E2E Driving

Your application is being processed

Research Intern: Closed-Loop E2E Driving

Job Number: P26INT-07
Honda Research Institute USA (HRI-US) is seeking a talented and motivated research intern to explore methods for improving closed-loop planning performance, spanning policy learning, trajectory generation, and closed-loop training and evaluation, and to validate these methods using state-of-the-art simulation benchmarks. This internship is ideal for Ph.D. students with a strong publication record in deep learning and planning who are looking to apply their research to practical robotics and autonomous systems.
Mountain View, CA

 

Key Responsibilities

 
  • Research and develop planning modules for E2E driving models, including trajectory scoring/selection, diffusion- or anchor-based trajectory generation, and multimodal planning heads.
  • Design closed-loop training strategies (e.g., closed-loop fine-tuning, RL/IL hybrid post-training, world-model-based rollout training) to mitigate distribution shift and compounding errors.
  • Build and run closed-loop evaluation pipelines on benchmarks such as NAVSIM, Bench2Drive, CARLA Leaderboard, or nuPlan; analyze failure modes and drive iterative improvement.
  • Conduct ablations and large-scale experiments; profile and optimize training/inference throughput on multi-GPU clusters.
  • Collaborate with full-time researchers and engineers; present findings internally and contribute to publications and/or patents.

 

Minimum Qualifications

 
  • Currently enrolled in a Ph.D. program in Robotics, Computer Science, Electrical Engineering, or a related field.
  • Solid foundation in deep learning and strong proficiency in Python and PyTorch.
  • Understanding of motion planning and/or policy learning fundamentals (imitation learning, reinforcement learning, or trajectory optimization).
  • Experience training and debugging neural networks at scale.
  • Ability to read, implement, and extend recent research papers independently.

 

Bonus Qualifications​

  • Publications at top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, CoRL, ICRA, RSS).
  • Hands-on experience with E2E driving frameworks (e.g., UniAD, VAD, PARA-Drive, DiffusionDrive, Hydra-MDP) or closed-loop benchmarks (NAVSIM, Bench2Drive, CARLA, nuPlan).
  • Experience with generative planners (diffusion policies, flow matching), world models, or RL post-training of driving policies.
  • Familiarity with simulation infrastructure, sensor simulation, or scenario generation.
  • Track record in autonomous driving challenges/leaderboards (e.g., CARLA Leaderboard, Waymo/nuPlan challenges).
  • Strong software engineering practices (CUDA/TensorRT optimization, distributed training, CI).

 

Years of Work Experience Required   0
Desired Start Date  June 2027
Internship Duration  3 Months
Position Keywords

 ​Autonomous driving, machine learining, artificial intelligence, end-to-end, closed loop

Alternate Way to Apply

Send an e-mail to careers@honda-ri.com with the following:
- Subject line including the job number(s) you are applying for 
- Recent CV 
- A cover letter highlighting relevant background (Optional)

Please, do not contact our office to inquiry about your application status.