Edge AI Engineer
Tasks
- Apply model quantization and pruning
- Build inference runtimes
- Build privacy preserving telemetry pipelines
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Evaluate privacy and bias
- Implement on device model update versioning and rollback
- Maintain technical documentation
- Optimize latency energy and memory
- Optimize models for accelerator backends
- Secure execution and model integrity verification
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Core ML | DSPs | Edge Cloud | Embedded Systems | Energy optimization | Federated Learning | Hybrid Edge Cloud | Integrity verification | Knowledge Distillation | Latency optimization | Machine Learning | Mobile GPUs | Mobile SoCs | Model Compression | Model Integrity | Model Integrity Verification | NPUs | ONNX Runtime | On Device Privacy | Profiling | Pruning | Python | Quantization | Secure execution | Telemetry | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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