NVIDIA
Senior Software Engineer, Humanoid Robotics
$152K–$288K
US, CA, Santa Clara · Senior · Full-time · Posted Sep 17, 2026
- Experience
- 5+ years of work related experience in software development related to robotics.
- Education
- Bachelor’s, Master’s or equivalent experience in computer science, robotics, computer engineering, or a related field.
About the job
Develop and deploy scalable humanoid robotics software solutions, focusing on application architecture, system integration, performance, and customer deployment.
What you'll do
- Build application architecture for humanoid robotics with architecture and software teams.
- Integrate and scale whole body controller and manipulation technologies; lead profiling, benchmarking, and verification.
- Guide resolution of functional and system performance issues with internal and external partners, and support customer deployments.
What they ask for
- 5+ years of work related experience in software development related to robotics.
- Bachelor’s, Master’s or equivalent experience in computer science, robotics, computer engineering, or a related field.
- Proven track record developing software products in machine learning and reinforcement learning, with hands-on physical robot experience and/or robotics conference publications.
- Proficiency in application development and scalability for autonomous machines; familiarity with robotics or automotive middleware; experience with robot simulation platforms and simulation-based reinforcement learning; proficient in Python and C/C++.
Nice to have
- Hands-on experience with humanoid robot or mobile manipulator hardware, particularly loco-manipulation and dexterous hand manipulation.
- Experience with ROS 2, Isaac SIM, Isaac Lab or equivalent; GPU programming or CUDA; NVIDIA Isaac or GPU hardware; open-source contributions or mentorship.
Pay
152,000 USD - 241,500 USD (base salary, Level 3); 184,000 USD - 287,500 USD (base salary, Level 4)
Tools
Python, C/C++, CUDA, ROS 2, Isaac SIM, Isaac Lab, NVIDIA Isaac
AI focus
Develop and integrate machine learning and reinforcement learning capabilities for autonomous robots, including simulation-based training and validation; sim-to-real transfer of RL policies is a plus.
Model-written summary · the original posting is authoritative