Embedded Autonomy Engineer

- Own bring-up and lifecycle of the Linux mission computer: board support packages, device trees, kernel configuration, and bootloaders on NVIDIA Jetson/Tegra and similar SoCs.

- Integrate high-bandwidth sensors into Linux end to end: MIPI CSI cameras with GMSL/FPD-Link SerDes, V4L2, and ISP tuning, plus radar over PCIe or Ethernet; own time-sync and calibration across the sensor suite.

- Build and optimize the real-time sensor-to-GPU data path that feeds perception, localization, and inference: zero-copy, DMA, GPU memory management, and camera-to-inference latency.

- Stand up and maintain the on-target execution environment for autonomy and ML: CUDA/TensorRT runtime, GPU/CPU scheduling, edge containers, and the drivers and libraries the stack depends on.

- Build and customize the embedded Linux image (Yocto or L4T): kernel, userspace, and reproducible builds for fielded compute.

- Harden the compute-and-sensing platform for flight: power, thermal, SWaP, vibration, and boot reliability; instrument, reproduce, and root-cause issues on real platforms.

- Define software-hardware interface specs with electrical engineers during board design cycles; debug integration with logic analyzers, oscilloscopes, and UART.

- Contribute to the autonomy runtime on the mission computer (middleware, health monitoring, state management) as the engineer who knows the Linux compute layer best.

- 5+ years of embedded Linux development on custom hardware, with deep expertise in device tree configuration, BSP customization, and kernel bring-up.

- Camera and sensor integration on Linux: MIPI CSI and V4L2, plus GMSL or FPD-Link SerDes; comfortable owning the sensor-to-memory path.

- Experience with Yocto or L4T-based build systems; fluency in C, C++, Bash, and Python.

- Strong hardware debugging with logic analyzers, oscilloscopes, CAN, and UART; reads schematics and collaborates fluently with EEs.

- Track record taking Linux-based compute platforms from bring-up to real-world deployment, with real-time and performance optimization.

- Wants to work up the stack into autonomy and on-target inference, not only maintain the OS and boards.

- NVIDIA Jetson and the L4T stack; CUDA, TensorRT, GStreamer/DeepStream for on-target inference and media pipelines.

- ROS 2/copper-rs/dora-rs and integration of autonomy, perception, or localization software on the mission computer.

- Multi-sensor time-sync and calibration (PTP, hardware triggering)

- GPU pipeline and memory optimization, DMA, and zero-copy under real-time constraints.

- Experience in contested or degraded environments (RF denial, low-light/night, high-vibration platforms).