Parallel Systems is seeking an Embedded Software Engineer, Perception to develop the low-level software powering the perception sensing stack on our autonomous, battery-electric rail vehicles. You will own drivers, kernel components, and firmware to process high-throughput camera and lidar data from sensors into our perception pipeline in real time and at scale. The role sits at the intersection of Linux, ARM, and NVIDIA compute platforms, collaborating with the embedded platform team to bring up hardware, harden drivers, and optimize latency. This on-site position is based in Los Angeles, California, United States.

Responsibilities

W

Develop and maintain drivers, kernel modules, and firmware for embedded Linux on ARM and NVIDIA Jetson/Tegra platforms.
Write low-level systems software in Rust and C/C++ for perception-critical components, from board bring-up through production hardening.
Build, modify, and optimize drivers for high-throughput sensors such as cameras and lidar, including capture pipelines, buffering, and data integrity under sustained load.
Own the performance of sensing hardware and drive real-time optimizations — implementing timesync, reducing latency and jitter, eliminating dropped frames, and keeping multi-sensor pipelines deterministic under load.
Test and validate low-level sensor configuration changes across firmware and sensor revisions under different test conditions.
Profile and optimize GPU and kernel-level performance on NVIDIA platforms (CUDA, TensorRT, scheduling, memory/IO) to meet real-time constraints for perception workloads.

Responsibilities

W

Develop and maintain drivers, kernel modules, and firmware for embedded Linux on ARM and NVIDIA Jetson/Tegra platforms.
Write low-level systems software in Rust and C/C++ for perception-critical components, from board bring-up through production hardening.
Build, modify, and optimize drivers for high-throughput sensors such as cameras and lidar, including capture pipelines, buffering, and data integrity under sustained load.
Own the performance of sensing hardware and drive real-time optimizations — implementing timesync, reducing latency and jitter, eliminating dropped frames, and keeping multi-sensor pipelines deterministic under load.
Test and validate low-level sensor configuration changes across firmware and sensor revisions under different test conditions.
Profile and optimize GPU and kernel-level performance on NVIDIA platforms (CUDA, TensorRT, scheduling, memory/IO) to meet real-time constraints for perception workloads.

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