Edge AI Engineer
Tasks
- Apply model compression quantization pruning distillation
- Benchmark and profile models on target devices
- Build cross platform inference runtimes
- Build privacy preserving telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Design edge AI solutions optimized for hardware constraints
- Design hybrid edge cloud architectures
- Develop benchmarking suites for accuracy latency energy tradeoffs
- Evaluate bias and on device privacy
- Implement on device model update versioning rollback
- Implement secure execution model protection integrity verification
- Maintain technical documentation runbooks and operational procedures
- Optimize models for accelerator backends
- Tune model performance for latency energy and memory
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C++ | Core ML | DSPs | Device security | Edge Cloud | Edge Computing | Embedded accelerators | Energy Efficiency | Federated Learning | Hybrid Edge Cloud | Knowledge Distillation | Latency optimization | Machine Learning | Mobile GPUs | Mobile SoCs | Model Compression | Model Optimization | NPUs | ONNX Runtime | On-device security | Privacy | Profiling | Pruning | Python | Quantization | Telemetry | TensorFlow Lite
Education
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