Edge AI Engineer
Tasks
- Apply quantization pruning distillation and model compression
- Build cross platform inference runtimes for edge devices
- Build privacy preserving telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Design edge AI solutions optimized for mobile and embedded hardware
- Design hybrid edge cloud architectures
- Develop benchmarking suites for accuracy latency and energy trade offs
- Evaluate responsible AI for privacy and bias
- Implement on device model update versioning and rollback
- Implement secure execution paths and model integrity verification
- Maintain technical documentation and runbooks
- Optimize models for accelerator backends
- Stay current with edge AI hardware and software updates
- Tune model latency energy and memory on target hardware
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C++ | Core ML | DSPs | Federated Learning | Hardware optimization | Knowledge Distillation | Machine Learning | Mobile GPUs | Model Compression | Model Rollback | Model versioning | NPUs | ONNX Runtime | Performance Profiling | Privacy | Pruning | Python | Quantization | Security | Telemetry | TensorFlow Lite
Education
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