Embedded AI Engineer, On-Device Models
Tasks
- Add device telemetry and benchmarking
- Advise research on edge friendly model architectures
- Benchmark and prevent regressions before release
- Build over the air update mechanisms
- Collaborate with silicon and device vendors on SDK integration
- Compile models for embedded targets
- Develop performance critical embedded runtime code
- Implement model packaging and deployment pipelines
- Integrate edge inference runtimes
- Map model graphs to CPU GPU NPU accelerators
- Optimize models for on-device inference
- Validate latency accuracy power memory and resource utilization
Perks/Benefits
- N/A
Skills/Tech-stack
Bare Metal | C# | C++ | CPU | Code Signing | DSP | Edge AI | Embedded Linux | Encrypted storage | Executorch | Fixed point | FreeRTOS | GPU | Integer arithmetic | Knowledge Distillation | Mixed Precision | Model Packaging | Model Security | Model compilation | NPU | ONNX Runtime | Operator fusion | Over-The-Air | Over-the-Air Updates | Pruning | Quantization | Rust | Secure deployment | TensorFlow Lite | TensorRT | Zephyr
Education
N/A
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