Research Intern: Learning Based Motion Planning

Honda Research Institute USA; Mountain View (CA); Presencial; USD 34.000 - 48.000; Jornada completa

Descripción de la vacante

Honda Research Institute USA in Mountain View, CA seeks a motivated Research Intern in Learning Based Motion Planning to develop software modules for autonomous driving research.

The internship involves training generative models, building evaluation pipelines, and collaborating with researchers to publish findings. Start May 2027 for a 3-month term. Mountain View location preferred.

Formación

• PhD candidate in robotics or related field.
• Strong Python and PyTorch skills.
• Research in ML, decision-making, motion planning, and robotics.
• Excellent Python/C++ programming and ROS experience.
• Experience with ML frameworks PyTorch or TensorFlow.

Responsabilidades

• Train and distill a generative model for the end-to-end autonomous driving task.
• 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.

Conocimientos

Python

PyTorch

Deep learning

Robotics

C++

ROS

Research experience

Educación

Ph.D. candidate in Robotics

Herramientas

ROS

CUDA

TensorRT

TensorFlow

Descripción del empleo

Research Intern: Learning Based Motion Planning - Honda Research Institute USA

Research Intern: Learning Based Motion Planning

Job Number: P26INT-05

Honda Research Institute USA (HRI-US) is seeking a motivated and detail-oriented research intern to develop reliable software modules for autonomous driving and cooperative mobility research. The intern will work on generative models for end-to-end autonomous driving.

Mountain View, CA

Key Responsibilities

• Train and distill a generative model for the end-to-end autonomous driving task.
• 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.
• Research experience in machine learning, decision-making, motion planning, and robotics.
• Excellent programming skills in Python and/or C++, and experience with ROS.
• Experience with ML frameworks such as PyTorch or TensorFlow.

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

May 2027

Internship Duration

3 Months

Position Keywords

Autonomous driving, E2E, machine learning, artificial intelligence