Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 7
Algorithms
CUDA @ 3
Communication @ 9
Debugging @ 6
Deep Learning @ 6
JAX @ 6
Machine Learning @ 4
Mentoring
PyTorch @ 6
Python @ 9
Reinforcement Learning
Robotics @ 4
Technical Leadership
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA's Seattle Robotics Lab conducts fundamental and applied robotics research across perception, planning, control, reinforcement learning, imitation learning, and simulation. The research is intended to advance robotics paradigms, transfer into NVIDIA's robotics and simulation products, and create new robotics markets. The role provides technical leadership and hands-on contributions across robotics and machine learning, with collaboration spanning research, product management, and engineering teams.
Responsibilities
- Develop algorithms, models, and methods for robotic manipulation and loco-manipulation for industrial and household applications.
- Integrate methods into real-world robotic manipulation systems, including collaborative and industrial robot arms, mobile manipulators, humanoids, and dexterous hands.
- Co-lead and contribute to multi-person research projects across the robotics and machine learning stack.
- Engage with the academic community through high-impact publications, conferences, workshops, and code releases.
- Collaborate with product managers and engineering teams to transfer research into NVIDIA products.
- Mentor interns and junior research scientists and engineers.
Requirements
- PhD in Robotics, Machine Learning, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field, or equivalent experience.
- At least 3 years of research experience after completing a PhD; 5 or more years is preferred.
- Deep knowledge of the theory and practice of robotics and AI, with an interest in real-world robotics applications.
- Proven research excellence, including publications 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 experience working as both a leader and contributor.
- Exceptional programming skills in Python. Familiarity with C++, CUDA, and Warp is a plus.
- Fluency in modern deep learning frameworks such as PyTorch and JAX.
- Significant experience with robotics frameworks such as ROS2 and physics simulation frameworks such as Isaac Sim, Isaac Lab, and MuJoCo.
- Ability to work through complex simulation and real-world robotics challenges, including debugging physics simulators and renderers, setting up and maintaining robotics hardware, debugging communication systems, and designing robust workflows for model training and evaluation.
Preferred Experience
- Transferring research into products in collaboration with product managers and engineering teams.
- Mentoring junior researchers, engineers, and interns.
- Bimanual and dexterous manipulation.
- Mobile manipulation and humanoid loco-manipulation.
- Multisensory perception, including vision, tactile, and force/torque sensing.
- Simulation, sim-to-real, and real-to-sim.
- Vision-language-action models, including architectural advancements, large-scale training, and test-time reasoning.
- Industrial applications such as bin-picking, kitting, and assembly.
Benefits
- Equity and benefits are provided.
- The Seattle Robotics Lab is located in an office in Seattle's Fremont neighborhood.
Applications will be accepted at least until July 24, 2026. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.
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