Senior System Software Engineer - Robotics

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior System Software Engineer focused on integrating and deploying AI models (foundation models, embodied AI, RL policies) into humanoid robot systems, optimizing system performance, and developing validation workflows. This role involves working with robotics, systems, AI, and simulation experts to enable real-world deployment of embodied AI.

What you'd actually do

  1. Drive end-to-end integration of robotics software stacks, including perception, control, learning-based policies, and runtime systems on real robots.
  2. Enable and support the deployment of foundation models, embodied AI models, and reinforcement learning (RL) policies on humanoid platforms.
  3. Develop and implement robot validation, testing, and benchmarking workflows spanning simulation and real hardware.
  4. Measure and optimize critical system-level metrics including latency, determinism, throughput, reliability, and performance.
  5. Work closely with multi-functional teams (research, simulation, hardware, platform, and SQA teams) to bring up and harden humanoid robotic systems.

Skills

Required

  • BS, MS, or PhD degree in Computer Science, Electrical Engineering, Computer Engineering, or related field (or equivalent experience).
  • 5+ years of development experience in researching, designing, and prototyping robotic system software.
  • Good understanding of real-time control systems, Linux kernel internal, various device driver models, arm architecture, and system design trade-offs.
  • Good understanding of system-level architecture, such as interconnects, memory hierarchy, interrupts, and memory-mapped IO.
  • Excellent programming and debugging skills in C, C++ and Python.
  • Strong system software engineering skills combined with a strive to solve hard problems.
  • Strong communication skills and ability to work across teams.

Nice to have

  • Experience with ROS (middleware, ecosystem, development, debugging tools).
  • Previous experience with CUDA.
  • 2+ years of hands-on development and field experience with production robots.

What the JD emphasized

  • humanoid robots
  • embodied AI
  • foundation models
  • reinforcement learning (RL) policies
  • real robots
  • production robotic systems
  • robot validation, testing, and benchmarking workflows
  • latency, determinism, throughput, reliability, and performance
  • humanoid robotic systems
  • field validation
  • system software quality

Other signals

  • foundation models
  • embodied AI
  • reinforcement learning (RL) policies
  • humanoid robots