Edge AI Engineer
Main mission
Deploys AI on embedded devices: mobile, sensors, vehicles.
5 key responsibilities
- Compress and optimize models for embedded systems: quantization, distillation, pruning.
- Deploy AI on mobile, sensors, cameras, and vehicles.
- Balance accuracy, latency, memory, and energy consumption.
- Integrate hardware accelerators (NPU, embedded GPU).
- Test robustness in real-world conditions: network, temperature, battery.
Key skills
TensorFlow Lite, ONNX, quantization, embedded hardware, C++, real-time constraints.
What's expected
Fit intelligence within physical constraints: every millisecond and milliwatt counts.
Career paths
Edge Architect, Autonomous Systems Engineer, AI Semiconductor Engineer.
Openings right now
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