Edge AI Engineer
Tasks
- Apply model distillation
- Apply model pruning
- Apply model quantization
- Build edge inference runtimes
- Build privacy preserving telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Conduct bias evaluation
- Conduct on device privacy evaluations
- Deploy models to mobile and embedded platforms
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Implement model protection and integrity verification
- Implement on device model updates versioning rollback
- Implement secure model execution paths
- Maintain technical documentation and runbooks
- Optimize model architectures for edge constraints
- Optimize models for accelerator backends
- Tune energy efficiency for target hardware
- Tune latency for target hardware
- Tune memory footprint for target hardware
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Core ML | DSP | Edge inference | Embedded Systems | Energy optimization | Federated Learning | GPU | Integrity verification | Knowledge Distillation | Latency optimization | Machine Learning | Mobile SoC | Model Compression | Model Integrity | Model Integrity Verification | Model Optimization | NPU | ONNX Runtime | Performance Profiling | Privacy-preserving | Privacy-preserving machine learning | Pruning | Python | Quantization | Secure execution | Telemetry | TensorFlow Lite
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