Atoms
Staff Systems Integration Engineer, Hardware-in-the-Loop (HIL)
$220K
San Francisco, CA +1 · Staff · Full-time · Posted Aug 31, 2026
- Location
- San Francisco, CA
San Francisco, California, United States - Experience
- 8+ years of relevant experience with a bachelor's degree, or 6+ years with a master's degree; equivalent hands-on experience may substitute for the degree.
- Education
- Bachelor's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, Computer Science, or a related field; master's degree is an alternative. Equivalent hands-on experience will be considered in place of the degree.
About the job
Owns end-to-end hardware-in-the-loop benches that validate the autonomy stack off-vehicle, including bench design, simulation, replay, automation, and system-level testing.
What you'll do
- Design, build, commission, and document system benches, including electrical architecture, harnesses, and correlation with vehicle behavior.
- Simulate absent vehicle systems and implement synchronized sensor-data replay, automated tests, and continuous-integration workflows.
- Validate fault and degraded-mode behavior; support cross-functional debugging, tooling decisions, suppliers, and engineering standards.
What they ask for
- Demonstrated experience designing and commissioning hardware-in-the-loop or system-level test benches.
- Hands-on vehicle-network experience, including restbus simulation, network database authoring, and bus analysis.
- Experience developing real-time plant, actuator, sensor, and power models for real-time target hardware.
- Proficiency in Python and a systems language (C, C++, or equivalent), plus test automation and continuous-integration experience.
Nice to have
- Working experience with robotics middleware and logging/replay tooling such as ROS or equivalent.
- Working knowledge of camera serialization (GMSL, FPD-Link), lidar and radar interfaces, and PTP/gPTP time synchronization.
Pay
$220,000-$280,00 per year (base salary)
Tools
CAN, CAN FD, Automotive Ethernet, Python, C, C++, ROS, PTP/gPTP
AI focus
Validates the autonomy stack for Physical AI and autonomous driving systems using hardware-in-the-loop benches, vehicle simulation, sensor-data replay, and fault testing.
Model-written summary · the original posting is authoritative