Robotics Research Intern - 2026

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 focused on fundamental and applied robotics research across the full robotics stack, including perception, planning, control, reinforcement learning, imitation learning, and simulation.
• 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 led many influential works that have been presented at top robotics, AI, and computer vision conferences; these works include BayesSim , cuRobo , DeXtreme , DiSECT , Factory , GraspNet , ITPS , MimicGen , RVT , 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 , and Isaac Manipulator.
• Furthermore, SRL collaborates closely with other research and engineering teams to advance and leverage the Cosmos and GR00T-N foundation models, as well as the Newton physics simulation engine.
• We are looking for a PhD research intern to play a pivotal role in our research efforts, providing deep, hands-on technical contributions.
• These 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.
• 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 robotics and AI companies.
• Note: This internship role will be based at NVIDIA's Zurich office.
• What you will be doing: Developing algorithms, models, and methods for robotic manipulation and/or loco-manipulation, for both industrial 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.
• What we need to see: Enrolled in a PhD 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 research track record, with work published in top robotics and AI conferences and journals such as RSS, CoRL, ICRA, IROS, IJRR, T-RO, 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 and JAX.
• Experience with robotics frameworks such as ROS2 and physics simulation frameworks such as Isaac Sim, Isaac Lab, MuJoCo, and/or Newton.
• 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 Multisensory perception (e.g., vision, tactile, and force/torque sensing) Simulation, sim-to-real, and real-to-sim Vision-language-action (VLA) models, including architectural advancements, large-scale training, and test-time reasoning Industrial applications, such as bin-picking, kitting, and assembly.