PhD Research Intern, Robotics - Summer 2027

Overview

• NVIDIA is at the forefront of the AI and robotics revolution, and NVIDIA’s robotics teams are on a mission to build the essential technology that can enable any company to become a robotics company.
• The Seattle Robotics Lab is uniquely positioned at the intersection of open academic research and real-world industry impact, pursuing fundamental and applied robotics research across the full robotics stack, including perception, planning, control, reinforcement learning, imitation learning, simulation, and robotics foundation models.
• This research aims to transform research paradigms, transfer into NVIDIA’s robotics and simulation products, and create new robotics markets for the world.
• The Seattle Robotics Lab has published over 500 research papers, including many influential works that have been presented at top robotics, AI, and computer vision conferences.
• These works include BayesSim , cuRobo , DeXtreme , DiSECT , Factory , GraspNet , IndustReal , ITPS , LAPA , MimicGen , NeRD , RoboLab , RVT , ScheduleStream , and SHAC.
• (See our publications page for a complete list.) The work has deeply impacted the research community and NVIDIA products, including Isaac Sim , Isaac Lab , Isaac Manipulator , Isaac Lab-Arena , Cosmos , and Newton amongst others.
• We are looking for PhD-candidate interns to play a pivotal role in our research efforts, providing deep, hands-on technical contributions.
• Our interns must have exceptional research and engineering skills, a strong research track record, and a team-first mindset.
• You will have the opportunity to achieve real impact, while working with some of the most creative, brilliant, and motivated researchers in the world.
• Interns can either be working on their own projects typically aligned with one of our internal efforts, or in some cases directly contribute to one of our efforts.
• Our former interns have gone on to become full-time researchers and engineers at NVIDIA and other world-leading tech companies, started academic careers at top universities, and launched frontier robotics and AI companies.
• What you will be doing: Developing algorithms, foundation models, and methods for robotic manipulation and/or loco-manipulation, for industrial, scientific, and household applications; Integrating these methods into real-world robotic manipulation systems, including those consisting of collaborative robot arms, industrial robot arms, mobile manipulators, humanoids, and dexterous hands; Contributing to multi-person research projects that require a diverse set of skills across the robotics and machine learning stack; Engaging with the academic community through high-impact publications, conferences, workshops, and/or code releases; Developing AI agents to accelerate research and engineering while ensuring correctness, reproducibility, and security.
• What we need to see: Enrolled in a PhD program in Robotics, Machine Learning, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field; Knowledge of both the theory and practice of robotics and AI, with a strong interest in connecting your work to real-world robotics applications; A demonstrated track record, with first-author work published in top robotics and AI conferences and journals such as RSS, CoRL, ICRA, IJRR, T-RO, Science Robotics, NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, and EMNLP; Exceptional communication, collaboration, and interpersonal skills, with significant experience working on a team; Exceptional programming skills in Python; in addition, familiarity with C++, CUDA, and Warp is a plus; Fluency in modern deep learning frameworks such as PyTorch or JAX; Experience in AI coding and research agents, including critical validation of their outputs; Experience with robotics frameworks such as ROS2 and physics simulation frameworks such as Isaac Sim, Isaac Lab, Newton, and/or MuJoCo; Comfort in working through the complexities of simulation and real-world robotics, including debugging physics simulators and renderers under rapid development; selecting, setting up, maintaining, and enhancing complex robotics hardware; debugging communication systems; and designing robust workflows for model training and evaluation.
• The following research areas and applications are of particular interest: Bimanual and dexterous manipulation Mobile manipulation and humanoid loco-manipulation Multimodal robot learning (e.g., vision, tactile, and force/torque sensing) Simulation (classical and neural), sim-to-real, real-to-sim, and world models Learning in the real world (e.g., imitation learning, RL) Robot foundation models (e.g., VLAs, WAMs, world models): pre-training, mid-training and post-training (SFT, RL, etc.) using various data sources (e.g., simulation, teleoperation, egocentric data) Agentic robotics Robot learning theory High-precision manipulation and reliable policy deployment Scientific lab automation.