Edge AI Engineer
Tasks
- Apply quantization and pruning
- Build inference runtimes
- Build privacy respecting telemetry pipelines
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Implement model update versioning and rollback
- Implement secure model execution paths
- Optimize for accelerator backends
- Optimize model latency and energy
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C++ | Cloud architectures | Core ML | DSPs | Device security | Edge inference | Edge/cloud architectures | Embedded Systems | Energy Efficiency | Federated Learning | Hardware acceleration | Hybrid Edge Cloud | Hybrid Edge Cloud Architectures | Knowledge Distillation | Latency optimization | Machine Learning | Memory Optimization | Mobile GPUs | Mobile SoCs | Model Compression | Model Pruning | Model Quantization | Model versioning | NPUs | ONNX Runtime | On Device Privacy | On-device security | Python | Rollback | Telemetry pipelines | TensorFlow Lite
Education
Roles
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