Edge AI Engineer
Tasks
- Apply model compression and quantization
- Build inference runtimes
- Build privacy respecting telemetry pipelines
- Collaborate with hardware and firmware teams
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Evaluate privacy and bias for responsible AI
- Implement on device model update versioning and rollback
- Implement secure execution and model integrity
- Maintain technical documentation
- Optimize accelerator backend performance
- Optimize latency energy and memory
- Translate edge AI advances into adoption recommendations
Perks/Benefits
Skills/Tech-stack
Architecture Optimization | Bias Evaluation | C++ | Core ML | DSP | Edge Deployment | Embedded Systems | Energy Efficiency | Federated Learning | Integrity verification | Knowledge Distillation | LLM optimization | Latency optimization | Machine Learning | Mobile GPU | Mobile SoC | Model Benchmarking | Model Compression | Model Integrity | Model Integrity Verification | NPU | Neural Network | Neural Network Architecture | Neural Network Architecture Optimization | ONNX Runtime | On Device Personalization | Performance Profiling | Privacy Protection | Pruning | Python | Quantization | Secure execution | Telemetry pipelines | TensorFlow Lite
Education
Roles
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