Machine Learning Engineer - Humanoid Robotics

Job description

NVIDIA is seeking exceptional machine learning engineers 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 define the future of humanoid robot capabilities. We are looking for strategic, ambitious, and creative 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 to define and execute projects in humanoid robotics loco-manipulation and mobile manipulation areas. Contribute to the development and advancement of GR00T and Cosmos foundation models. Develop reference workflows with Isaac Lab and Newton for humanoid and mobile manipulation dexterous tasks. Advance technologies for robot learning and synthetic data generation using human videos. Design, implement, and deploy novel algorithms for humanoid robot locomotion and manipulation in both simulated and real-world environments. Transfer innovations into products, with deliverables including prototypes, open source software contributions, patents, and/or publications in top conferences and journals. Drive the full development lifecycle from model and algorithm design, with sim-to-real transfer, to rigorous on-robot validation and production deployment. Collaborate cross-functionally with teammates and partners to share best practices and advance shared goals. What we need to see: This role prioritizes candidates with proven execution bandwidth of applied research and engineering and a strong delivery track record on robotics platforms. PhD or Master’s degree in Robotics, Computer Science, or a related field (or equivalent experience). 3+ years of experience working on robotics software. Experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow, and physics simulation tools like Isaac Sim/Lab or MuJoCo. Expertise in 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 of real robot testing, humanoid experience is preferred. Strong software engineering fundamentals, including proficiency in C++ and Python. Ways to stand out from the crowd: Experience learning from human video demonstrations or human-object reconstruction. Expertise in dexterous bimanual manipulation or whole-body control. Proven track record in robotics research, including publications in top conferences (e.g., RSS, ICRA, CoRL, NeurIPS, CVPR, ICLR). Demonstrated technical leadership experience.