> Markdown version of [/jobs/ext/4712047-embedded-platform-engineer](https://www.wearedevelopers.com/jobs/ext/4712047-embedded-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Embedded Platform Engineer - **Company:** Factbird - **Location:** Germany - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Vision, C++ (Programming Language), Cloud Computing, Software Debugging, DevOps, Linux on Embedded Systems, Linux Distribution, System Programming, Edge Inference, Hardware Testing, Yocto, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, Video Streaming, Drift Detection, Aws Iot - **Published:** November 14, 2026 - **Apply:** https://www.adzuna.de/details/5963537761 ## About the Role We're looking for an engineer who has shipped embedded Linux into production and lived with it afterwards: someone who has debugged a device they couldn't touch, and who wants real responsibility rather than a ticket queue. You work independently, but you don't work alone. You can take a loosely defined problem and move it forward without daily direction, and you pull others in early, because on a small team the best fix is one more than one person understands. You're disciplined where it matters, in releases and rollouts, and curious where it pays off, in vision and new hardware. You don't need everything below. Yocto is the core. Rust matters, but strong C/C++ engineers who have started with Rust are welcome too. The rest you can grow into with us. The ideal background * Hands-on experience shipping embedded Linux to production, including Yocto: layers, BSPs and images you've built and maintained yourself, not just consumed * Strong systems programming in Rust, or deep C/C++ experience on embedded Linux and real exposure to Rust that you want to build on * An advantage if you have worked with AWS IoT Core or Greengrass, video pipelines or ML inference on edge hardware, or in industrial settings where devices run unattended for years * A solid grasp of embedded security and update mechanisms: signed images, A/B or similar update schemes, and secure device provisioning * You can explain a technical trade-off clearly, whether you're talking to another engineer, Support or a customer, and you do it in fluent English * We welcome applications from people eager to move to Denmark, as long as you already have the legal right to live and work here. ## Description As Embedded Platform Engineer, you'll work hands-on across Factbird EDGE, our industrial edge platform, together with the rest of the IoT team: the operating system, the device software, and the vision models that run on it. You'll take ownership of the infrastructure around the devices: provisioning and recovery, over-the-air updates, and fleet observability, the systems that keep our unattended devices reliable, secure, and safely updatable in factories you may never visit. You'll work in Rust on embedded Linux, with Yocto, AWS IoT Greengrass, GStreamer and ONNX Runtime at the core, alongside our Cloud and DevOps engineers and the people building our AI capabilities. Part of the job is shaping the role with the team as our edge products grow. You'll thrive here if you like working across a system end to end, from the bootloader to the cloud connection, and if you'd rather fix the root cause in the build than patch around it on the device. What you'll do Embedded Linux & the EDGE platform * Develop our Yocto-based Linux distribution: layers, BSPs, kernel configuration, and reproducible builds * Build and maintain the Rust components that run on our edge devices * Bring up new hardware platforms, debug on target, and keep the platform secure and maintainable over a long device lifetime * Keep our CI and on-hardware testing healthy, so changes reach devices with confidence Edge inference & vision * Work with our ML and vision engineers on the device side of our computer vision capabilities: camera pipelines, video streaming, and model inference that runs efficiently on industrial hardware * Share ownership with our ML engineers of how trained models move from our model pipeline onto devices and how they perform there: packaging, rollout, rollback, and drift monitoring * Help bring new edge runtimes for vision workloads from development into production Fleet infrastructure & ownership * Own device provisioning, over-the-air updates, and fleet observability * Make deployments to devices in the field safe and boring: staged rollouts, health checks, and recovery paths * Help diagnose issues on devices at customer sites, working with Support, Product, and Cloud to turn them into lasting fixes * Share knowledge in both directions: learn the parts of the platform others built, and make sure what you build doesn't live only in your head