Edge AI Engineer
Tasks
- Apply quantization pruning distillation and architectural optimization
- Build cross platform inference runtimes
- Build privacy preserving telemetry pipelines
- Design and implement Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Drive responsible AI privacy and bias evaluation
- 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
- Stay current with edge AI hardware and software
- Tune model performance for latency energy efficiency and memory
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C++ | Core ML | DSP | Data Privacy | Device deployment | Edge inference | Embedded Systems | Energy Efficiency | Federated Learning | GPU | Hybrid Cloud | Integrity verification | Knowledge Distillation | Latency optimization | Machine Learning | Mobile SoCs | Model Compression | Model Optimization | Model Security | NPU | ONNX Runtime | On-device Deployment | Performance Profiling | Pruning | Python | Quantization | Telemetry | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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