Introduction
Embedded AI sits at the intersection of hardware and ML — a rare combination. Demand is surging in defence, manufacturing, automotive, and IoT. Supply has not caught up.
The Supply Problem
Most ML engineers train models in the cloud. They think in terms of batches, distributed training, and APIs. Embedded AI requires a different mindset: hardware constraints, real-time inference, quantization, and often C/C++ alongside Python. You need someone comfortable dropping down to firmware, understanding memory bandwidth, and optimizing for a specific chipset.
Universities teach cloud ML. Industry trains on Kaggle competitions. Neither teaches embedded AI at scale. Most embedded AI engineers came from robotics, firmware, or automotive backgrounds and taught themselves ML. These people are rare, expensive, and usually already employed.
Where the Demand Is
Defence tech is the biggest driver. Drone detection, autonomous systems, and threat identification all need on-device inference. Manufacturing wants real-time defect detection without sending images to the cloud. Automotive is building ADAS and autonomous driving stacks. Smart agriculture is deploying pest detection and crop monitoring. IoT companies need edge ML for smart devices. Every major tech company is hiring embedded AI engineers. The queue is long.
What to Look For
TinyML experience is a must. ONNX and TensorRT familiarity for model optimization. FPGA or edge TPU experience is a strong plus. Solid systems engineering skills — understanding memory management, real-time constraints, and power consumption. Ideally, someone who's deployed a model to an embedded system in production, not just in a lab. C/C++ fluency is table stakes. Python is secondary.
How to Attract Them
Interesting hardware problems. These engineers got into the field because they love building things that move or sense the real world. If you're just optimizing cloud inference, you won't win them. Competitive comp (AUD 165k-280k+ depending on seniority). Flexibility on remote work — embedded engineers often have roots in hardware-heavy cities and may have equipment at home. Access to real-world deployment. Let them ship code to production hardware, not just a development board.
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If you're hiring embedded AI talent or building a team in this space, let's talk. Sonitec specializes in hard-to-find technical roles and has placed embedded AI engineers across defence, manufacturing, and robotics.
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