Machine Learning Intern, Humanoid Robotics - 2026

NVIDIA is seeking exceptional machine learning interns to join our world-class robotics initiatives focused on humanoid loco-manipulation. As part of the Isaac Loco-Manipulation team, you’ll collaborate with industry-leading experts, contribute to robotics foundation models including GR00T and Cosmos, and help advance the future of humanoid robot capabilities. We are looking for ambitious, creative, and research-driven individuals passionate about advancing the boundaries of robotics. This is demanding, cross-disciplinary work at the intersection of cutting-edge research and rigorous engineering.

What you'll be doing:

• Collaborate with researchers and engineers on focused projects in humanoid robotics loco-manipulation and mobile manipulation areas.

• Support the development and advancement of GR00T and Cosmos foundation models.

• Help develop reference workflows with Isaac Lab and Newton for humanoid and mobile manipulation dexterous tasks.

• Advanced technologies for robot learning and synthetic data generation using human videos.

• Design, implement, and test novel algorithms for humanoid robot locomotion and manipulation in both simulated and real-world environments.

• Drive a scoped internship project from model/algorithm design and sim-to-real transfer through to on-robot validation, with the potential for open-source contributions or publications.

• Collaborate cross-functionally with teammates and partners to share findings and advance shared goals.

What we need to see:

• Currently pursuing a PhD or Master’s degree in Robotics, Computer Science, or a related field.

• Strong academic or project track record demonstrating execution bandwidth in applied research and engineering on robotics platforms.

• Hands-on experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow, and physics simulation tools like Isaac Sim/Lab or MuJoCo.

• Strong familiarity with foundation models for robotics and 3D perception.

• Experience with sim-to-real and real-to-sim transfer in robotics.

• Deep knowledge of robot learning, including imitation and reinforcement learning.

• Hands-on experience with real robot testing; humanoid experience is preferred.

• Strong software engineering fundamentals, including proficiency in C++ and Python.

Ways to stand out from the crowd:

• A proven track record in robotics research, including publications in top conferences (e.g., RSS, ICRA, CoRL, NeurIPS, CVPR, ICLR).

• Experience learning from human video demonstrations or human-object reconstruction.

• Expertise/research focus in dexterous bimanual manipulation or whole-body control.