Edge AI Engineer
Tasks
- Apply quantization pruning distillation model optimization
- Build inference runtimes with TensorFlow Lite ONNX Runtime and Core ML
- Build privacy preserving telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Design edge AI solutions for mobile and embedded hardware
- Design hybrid edge cloud architectures
- Develop benchmarking suites for accuracy latency and energy
- Evaluate privacy and bias for responsible AI
- Implement on device model update versioning and rollback
- Implement secure execution model protection and integrity verification
- Optimize models for DSP NPU and mobile GPU backends
- Tune model latency energy efficiency and memory
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Core ML | DSP | Federated Learning | Knowledge Distillation | Machine Learning | Mobile GPUs | Model Compression | Model Deployment | NPUs | ONNX Runtime | On Device Privacy | Performance Tuning | Profiling | Pruning | Python | Quantization | Telemetry | TensorFlow Lite
Education
Roles
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