Embedded AI Engineer
Tasks
- Apply model compression and quantization
- Build inference runtimes
- Build privacy preserving telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Evaluate privacy and bias
- Implement on device model updates and rollback
- Implement pruning and distillation
- Implement secure model protection and integrity verification
- Integrate accelerator backends
- Maintain technical documentation
- Optimize for latency energy and memory
Perks/Benefits
Skills/Tech-stack
Benchmarking | Bias Evaluation | C++ | Core ML | DSP | Edge AI | Energy Efficiency | Federated Learning | Hardware acceleration | Inference Optimization | Knowledge Distillation | Latency optimization | Machine Learning | Memory Optimization | Mobile SoC | Model Compression | Model versioning | NPU | ONNX Runtime | On Device Privacy | Pruning | Python | Quantization | Secure execution | Telemetry pipelines | TensorFlow Lite
Education
N/A
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