Senior Embedded Software Engineer - Edge & On-Orbit Compute
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Job overview
SkyServe is seeking a Senior Embedded Software Engineer to manage mission-critical software on satellite edge hardware. The engineer will build secure container runtimes, optimize computer vision deployments on heterogeneous accelerators, and support fault-tolerant, power-efficient execution in space conditions. The role spans embedded systems, on-orbit AI deployment, security, and mission-readiness testing.
Skills & qualifications
Skills
Qualifications
Full job description
About Us SkyServe is pioneering edge-native satellite architectures and orbital artificial intelligence. Through our flagship software stack—STORM (space-grade edge OS and flight container runtime) and SURGE (on-orbit AI acceleration and smart-downlink engine)—we run autonomous algorithms directly on satellite payload compute modules in Low Earth Orbit (LEO). Position Overview We are seeking a Senior Embedded Software Engineer to own the deployment, security, and lifecycle management of mission-critical software running on extreme edge hardware. You will design hardened, containerized execution runtime environments, optimize computer vision deployments on heterogeneous accelerators, and ensure fault-tolerant, power-efficient execution in the harsh space radiation and thermal environment. Key Responsibilities
- Secure & Resilient Edge Containerization
- Architect, configure, and maintain secure, lightweight container execution environments (Docker, Podman, containerd) optimized for resource-constrained embedded systems and spaceflight hardware.
- Implement isolation, sandboxing, and resource fencing (cgroups, namespaces, seccomp, AppArmor) to run multi-tenant customer applications and onboard computer vision pipelines without risking flight avionics stability.
- Design robust, failsafe Over-the-Air (OTA) delta updates, atomic image rollbacks, and recovery mechanisms over low-bandwidth, intermittent satellite uplink/downlink passes.
- Embedded Systems & Hardware Interfacing
- Develop, debug, and optimize systems software in C and Modern C++ across embedded Linux systems (Yocto, Buildroot, Ubuntu Core) and Real-Time Operating Systems (RTOS).
- Interface high-throughput satellite payloads (hyperspectral, multispectral, RGB optical, and SAR sensors) with onboard processing units using high-speed protocols (PCIe, MIPI CSI-2, GigE Vision, USB 3.0, CAN, SPI, UART).
- Implement hardware health monitors, thermal throttling controls, watchdog services, and autonomous recovery routines against Single Event Effects (SEEs/SEUs) caused by space radiation.
- On-Orbit Computer Vision & AI Deployment
- Package, quantize, and deploy deep learning and computer vision models (object detection, small-target maritime tracking, cloud segmentation, feature extraction) directly onto embedded edge accelerators.
- Optimize inference performance and memory bandwidth on hardware platforms such as NVIDIA Jetson (Orin/Xavier), Edge TPUs, ARM Ethos/Mali, and FPGA-based neural accelerators utilizing TensorRT, ONNX Runtime, CUDA, or TVM.
- Benchmark and profile end-to-end vision pipelines (preprocessing pipelines, CV/DL models, compression etc.).
- Verification, Testing & Mission Readiness
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Conduct Hardware-in-the-Loop (HIL) testing and flat-sat benchmarking using flight-representative System-on-Chips (SoCs) and payload emulators.
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Establish automated CI/CD validation pipelines to cross-compile, build, test, and sign containerized images for orbital target architectures. What We Are Looking For Must-Have Qualifications:
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Experience: 4+ years of hands-on embedded software engineering experience designing, deploying, and operating software on embedded Linux platforms.
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Systems Programming: Strong command of C and C++ (11/14/17) and Python, with a deep understanding of memory management, POSIX threads, IPC mechanisms, and kernel-user space boundaries.
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Edge Containers & Linux Internals: Deep familiarity with container runtimes (Docker/Podman/containerd) at the systems level, systemd services, filesystem layouts (squashfs, overlayfs), rootfs customization, and Yocto/Buildroot.
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Computer Vision & Edge ML: Direct experience porting and deploying computer vision models (OpenCV, TensorRT, ONNX Runtime) onto embedded edge hardware (e.g., NVIDIA Jetson platform).
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Embedded Security & Networking: Practical understanding of public key infrastructure (PKI), image signing, secure boot, TLS/mTLS, encrypted filesystems, and sandboxing. Nice-to-Have / Strong Advantages:
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Direct exposure to NewSpace systems, satellite avionics, CubeSat payloads, or Command & Data Handling (C&DH) interfaces.
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Experience with zero-trust architectures or air-gapped system maintenance for edge devices.
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Understanding of radiation effects on commercial-off-the-shelf (COTS) electronic components and mitigations at the software layer.
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Familiarity with hardware acceleration frameworks for vision pipelines (V4L2, GStreamer, DeepStream).
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