Senior Research Engineer, Robotics Systems

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior/Principal Engineer in robotics systems, focusing on foundation models and full-stack technology for humanoid robots. Responsibilities include designing teleoperation software, optimizing control stacks, deploying neural network models on hardware, and collaborating on the MLOps lifecycle. Requires strong robotics and software engineering background, with experience in real-time control and deploying ML models on robotic hardware.

What you'd actually do

  1. Design and maintain teleoperation software for controlling humanoid robots with low latency and high precision;
  2. Develop and optimize the control stack, including locomotion, manipulation, and whole-body control algorithms;
  3. Deploy and evaluate neural network models in physics simulation and on real humanoid hardware;
  4. Implement tools and processes for regular robot maintenance, diagnostics, and troubleshooting to ensure system reliability;
  5. Monitor teleoperators at the lab and develop quality assurance workflows to ensure high-quality data collection;

Skills

Required

  • Bachelor's Degree in Computer Science, Robotics, Engineering, or related field or equivalent experience
  • 8+ years of full-time industry experience in robotics hardware or software full-stack
  • Hands-on experience with deploying and debugging neural network models on robotic hardware
  • Ability to implement real-time control algorithms, teleoperation stack, and sensor fusion
  • Proficiency in languages such as Python, Rust, C++
  • Experience with robotics frames (ROS)
  • Experience with physics simulation (Gazebo, Mujoco, Isaac, etc.)
  • Experience in maintaining and troubleshooting robotic systems, including mechanical, electrical, and software components

Nice to have

  • Master’s or PhD’s degree in Computer Science, Robotics, Engineering, or a related field
  • Experience at autonomous driving or humanoid robotics companies on real hardware deployment
  • Experience in robot hardware design
  • Demonstrated Tech Lead experience, coordinating a team of robotics engineers and driving projects from conception to deployment
  • Contributions to popular open-source robotics frameworks or research publications in top-tier conferences, such as ICRA, IROS, RSS, CoRL

What the JD emphasized

  • foundation models
  • humanoid robots
  • real humanoid hardware deployment
  • robotics hardware or software full-stack
  • deploying and debugging neural network models on robotic hardware
  • real-time control algorithms
  • teleoperation stack
  • sensor fusion
  • ROS
  • physics simulation
  • robot maintenance, diagnostics, and troubleshooting
  • quality assurance workflows
  • model training
  • data processing
  • MLOps lifecycle

Other signals

  • foundation models for humanoid robots
  • large-scale robot learning
  • embodied AI
  • physics simulation
  • real humanoid hardware deployment