Edge AI Engineer
Tasks
- Apply model compression quantization pruning distillation
- Build cross platform inference runtimes
- Build privacy preserving telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Design edge AI solutions for constrained devices
- Design hybrid edge cloud architectures with graceful degradation
- Develop benchmarking suites for accuracy latency energy tradeoffs
- Evaluate on device privacy and bias
- Implement on device model update versioning and rollback
- Implement secure execution model protection and integrity verification
- Maintain technical documentation and runbooks
- Optimize inference for latency energy and memory
- Optimize models for DSP NPU and mobile GPU backends
- Stay current with edge AI hardware and software developments
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Cloud architectures | Core ML | DSP | Edge/cloud architectures | Energy Efficiency | Federated Learning | Hybrid Edge Cloud | Hybrid Edge Cloud Architectures | Integrity verification | Knowledge Distillation | Latency optimization | Machine Learning | Mobile GPU | Model Benchmarking | Model Compression | NPU | ONNX Runtime | On Device Privacy | Profiling | Pruning | Python | Quantization | Security | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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