Research Intern: Closed-Loop E2E Driving

Honda Research Institute USA; Mountain View (CA); On-site; USD 55,000 - 83,000; Full time

Job summary

Honda Research Institute USA in Mountain View is offering a research internship focused on improving closed-loop planning for autonomous driving. The role targets Ph.D.

students with strong publications in deep learning and planning, to explore policy learning, trajectory generation, and evaluation on modern simulators. The internship emphasizes collaboration with full-time researchers, writing up results, and contributing to open publications and patents.

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.

Responsibilities

• Research and develop planning modules for E2E driving models, including trajectory scoring/selection and multimodal planning heads.
• Design closed-loop training strategies to mitigate distribution shift and errors.
• Build and run evaluation pipelines on NAVSIM, Bench2Drive, CARLA Leaderboard, or nuPlan; analyze failures.
• Collaborate with researchers; present findings and contribute to publications/patents.

Skills

Deep learning

PyTorch

Python

Reinforcement learning

Motion planning

Education

PhD candidate in Robotics/CS/EE

Tools

CUDA

CARLA

Job description

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

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