Drimva

General Motors

Senior Software Engineer, Rendering Infrastructure (AV Simulation)

$153K–$234K

Sunnyvale, California, United States of America · Remote · Senior · Full-time · Posted Aug 17, 2026

Experience
5+ years of professional software engineering experience; substantial portion in performance-critical systems software.
Education
Bachelor's degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience.

About the job

Build and optimize the infrastructure connecting a GPU-accelerated sensor simulator to autonomous-vehicle software and compute clusters for deterministic, large-scale closed-loop simulation.

What you'll do

  • Integrate the renderer with AV stacks, learned driving models, and cloud or on-premises execution environments.
  • Improve deterministic execution, sensor timing fidelity, and multi-world GPU performance.
  • Optimize runtime deployment, asset delivery, caching, profiling, and worker start-up.

What they ask for

  • Bachelor's degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience.
  • 5+ years of professional software engineering experience, with a substantial portion focused on performance-critical systems software.
  • Production proficiency in modern C++ (C++17/20), working proficiency in Python, and strong Linux systems programming skills.
  • Depth in robotics/autonomous systems or GPU programming, plus experience designing, implementing, and debugging reliable distributed systems.

Nice to have

  • Experience with AV sensor-feed timing and closed-loop simulation perturbations.
  • Experience with single-GPU multi-scenario execution, Linux deployment optimization, shader or texture caching, or OpenUSD runtime optimization.

Pay

$153,200 to $234,100 (base salary)

Tools

C++17/20, Python, Linux, ROS/ROS 2, PyTorch, CUDA, NVIDIA OptiX, OpenUSD

AI focus

Integrate the simulator with PyTorch-based learned driving models and support perception training and reinforcement learning.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 4, 2026 (UTC)