Edge AI Engineer
Tasks
- Apply quantization pruning distillation and architectural optimization
- 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 with graceful degradation
- Develop benchmarking suites for accuracy latency and energy
- Evaluate responsible AI privacy and bias
- Implement on device model update versioning and rollback
- Implement secure execution paths model protection and integrity verification
- Maintain technical documentation and runbooks
- Optimize models for accelerator backends
- Translate edge AI hardware and software updates into adoption proposals
- Tune model performance for latency energy efficiency and memory
Perks/Benefits
Skills/Tech-stack
Benchmarking | C++ | Core ML | Data Privacy | Edge AI | Energy Efficiency | Federated Learning | Hardware acceleration | Knowledge Distillation | Latency optimization | Machine Learning | Model Compression | Model Deployment | ONNX Runtime | On Device Privacy | Performance Profiling | Pruning | Python | Quantization | Secure execution | TensorFlow Lite
Education
Roles
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