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. 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
