The embedded engineer’s full toolkit
Embedded engineering is a craft first, and that is what this track is built around: embedded C, real-time operating systems, the memory and timing model you hold in your head, and embedded systems architecture for designing software that copes with the quirks of real hardware. This is the spine of the catalogue and most of what an embedded engineer spends a career getting good at.
Around that we teach the disciplines that keep hardware projects honest: test-driven development with a harness that runs on-target or in a faithful simulator, continuous integration against real cross-toolchains, version control built for hardware-software teams, and the agile, safety and security practices that hold a project together when the cost of a defect is a field recall rather than a hotfix. None of this is glamorous and all of it is load-bearing – it is the difference between firmware that ships and firmware that limps.
AI tooling enters where it genuinely helps. The tool training – Claude Code, Cursor, GitHub Copilot, JetBrains Junie, in embedded-flavoured variants – is taught against firmware-shaped problems, where a plausible-but-wrong suggestion costs a session on real hardware rather than a failed unit test, and always next to the verification and review disciplines above rather than as a substitute.
This is the part of the catalogue closest to where our trainers come from: two decades in safety-critical embedded, plus the Agile Embedded and Embedded AI podcasts. If AI-on-device is explicitly your direction, the Embedded AI school curates those variants and the Embedded AI development path sequences them; otherwise the broader embedded catalogue stands on its own.