Edge AI Engineer
Tasks
- Apply model compression techniques
- Build edge inference runtimes
- Build privacy aware telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Deploy ML models to mobile and embedded platforms
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Evaluate bias and privacy considerations
- Implement on device model update and rollback
- Maintain technical documentation
- Optimize models for latency energy and memory
- Quantize and prune machine learning models
- Secure model execution and integrity verification
Perks/Benefits
Skills/Tech-stack
C++ | Core ML | DSPs | Edge inference | Embedded Systems | Federated Learning | Knowledge Distillation | Machine Learning | Mobile GPUs | Mobile SoCs | Model Benchmarking | Model Compression | Model Deployment | NPUs | Network Optimization | Neural Network | Neural Network Optimization | ONNX Runtime | Performance Engineering | Privacy | Profiling | Pruning | Python | Quantization | Security | Telemetry pipelines | TensorFlow Lite
Education
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