Embedded Software Engineer, Perception (All Levels)

Embedded Software Engineer, Perception (All Levels)

Parallel Systems is seeking an experienced Firmware Engineer to build the low-level software that powers the perception sensing stack on our fully autonomous, battery-electric rail vehicles. In this role, you'll own the drivers, kernel components, and firmware that get high-throughput camera and lidar data off the sensor and into our perception pipeline reliably, in real time, and at scale. You'll work at the boundary of Linux, ARM, and NVIDIA compute platform, partnering closely with the embedded platform engineers who maintain our custom Linux platforms to bring up hardware, harden drivers, and squeeze every millisecond of latency out of our sensing pipeline. If you like living close to the kernel, the hardware, and the timing budget, we'd love to work with you. 

We are open to candidates across a range of experience levels, excluding recent graduates. 

Responsibilities 

• Develop and maintain drivers, kernel modules, and firmware for embedded Linux systems running on ARM and NVIDIA (Jetson/Tegra) compute 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 camera, lidars, IMUs, including capture pipelines, buffer management, 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 to confirm correct behavior 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. 

What Success Looks Like: 

• After 30 Days: You've developed a working understanding of our embedded Linux stack, NVIDIA Tegra/Jetson build system, and sensor architecture. You've identified initial performance bottlenecks and created a development plan to address them. 
• After 60 Days: You've landed driver or kernel-level improvements on at least one sensor pipeline, contributed hands-on to the Yocto/Tegra build alongside the embedded platform team, and built diagnostics for latency, dropped frames, and timing drift. You're actively contributing to the real-time system that handles sensor data ingestion and feeds ML model inference in production. 
• After 90 Days: You own a driver or firmware subsystem end-to-end, with a measurable reduction in latency and jitter and improved timesync across the camera/lidar pipeline. You're contributing to the real-time perception pipeline, including the system handling sensor data and ML model inference, and to GPU/kernel optimization work with clear impact on overall perception throughput. 

Basic Requirements: 

• Bachelor's or higher degree in Computer Science, Electrical Engineering, or a related technical discipline 
• 4+ years of hands-on experience in embedded, firmware, or systems software engineering. 
• Strong knowledge of Linux internals and driver development on ARM-based platforms. 
• Proficiency in Rust and C++ for systems-level programming. 
• Experience with NVIDIA embedded platforms (Jetson/Tegra) 
• Experience developing or maintaining drivers for high-throughput sensors such as cameras and/or lidar, including data capture, buffering, and timestamp synchronization. 
• Comfortable debugging across the hardware/software boundary (device tree, kernel logs, oscilloscope, logic analyzer). 
• Excellent communication and collaboration skills, with experience working on interdisciplinary teams. 

Preferred Qualifications: 

• Experience with GPU and kernel-level optimization on NVIDIA platforms (CUDA, TensorRT). 
• Experience with Yocto-based builds. 
• Hands-on experience with V4L2, GStreamer, MIPI CSI-2 camera stacks. 
• Experience in autonomous vehicles, robotics, or other safety-critical domains. 
• Familiarity with ROS2, sensor fusion, or SLAM. 
• Knowledge of and experience contributing to real-time perception streaming pipelines, including GStreamer-based media pipelines.