Edge AI Engineer
Tasks
- Apply quantization and pruning techniques
- Build cross platform inference runtimes
- Build privacy preserving telemetry pipelines
- Design edge AI solutions for mobile and embedded hardware
- Design hybrid edge cloud architectures
- Develop benchmarking suites for accuracy latency and energy
- Implement on device model update versioning and rollback
- Implement secure execution model protection and integrity verification
- Maintain technical documentation and operational runbooks
- Optimize model latency energy and memory
- Optimize models for accelerator backends
- Perform on device privacy and bias evaluation
Perks/Benefits
Skills/Tech-stack
Bias Evaluation | C++ | Core ML | DSPs | Edge Cloud | Embedded Systems | Energy Efficiency | Federated Learning | Hybrid Edge Cloud | Knowledge Distillation | Latency optimization | Machine Learning | Memory Footprint | Mobile GPUs | Mobile SoCs | Model Compression | Model Deployment | Model Optimization | NPUs | ONNX Runtime | On-device Inference | Performance Profiling | Privacy & Security | Pruning | Python | Quantization | Telemetry pipelines | TensorFlow Lite
Education
Roles
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