Edge / Embedded (ARM) Jobs in Computer Vision
Browse CV roles that require Edge / Embedded (ARM) across all industries and experience levels.
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What is Edge / Embedded (ARM)?
Edge and embedded ARM deployment runs inference on low-power processors built into cameras, sensors and appliances. Memory, thermal and power budgets are tight, so model compression and careful runtime selection are central rather than optional.
Where Edge / Embedded (ARM) is used
Smart cameras, battery-powered field sensors, retail analytics devices and industrial gateways — anywhere a device must process locally because connectivity or bandwidth cannot be relied on.
Roles that ask for Edge / Embedded (ARM)
- Embedded Vision Engineer
- Edge AI Engineer
- Firmware Engineer, ML
- Computer Vision Engineer
- ML Systems Engineer
Related skills & tools
Edge / Embedded (ARM) jobs — common questions
What is the hardest part of embedded vision work?
Fitting within the budget without losing accuracy. That means quantisation, pruning, architecture choices made for the hardware, and often accepting a smaller input resolution — then proving the result still meets the product requirement.
Which runtimes are used on ARM?
TFLite with the appropriate delegate, ONNX Runtime, NCNN and vendor-specific SDKs. The right choice depends heavily on the SoC and whether it has an NPU or DSP worth targeting.
Why is this a well-paid specialisation?
It requires machine learning, C++ and hardware understanding simultaneously, and the constraints leave no room for the brute-force approaches that work in the cloud. Engineers who can ship reliably at the edge are genuinely scarce.