Edge AI Engineer
Tasks
- Apply model compression and quantization
- Build cross platform inference runtimes
- Build privacy preserving telemetry pipelines
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Drive responsible AI evaluation
- Implement on device model update and rollback
- Implement secure execution paths for edge models
- Optimize model performance for latency and energy
- Optimize models for accelerator backends
Perks/Benefits
- N/A
Skills/Tech-stack
Architecture Optimization | Benchmarking | Bias Evaluation | C++ | Core ML | DSP | Edge Computing | Embedded Systems | Energy Efficiency | Federated Learning | Knowledge Distillation | Latency optimization | Machine Learning | Mobile SoC | Model Architecture | Model Architecture Optimization | Model Compression | Model Deployment | Model versioning | NPU | ONNX Runtime | On Device Privacy | Performance Engineering | Privacy | Profiling | Pruning | Python | Quantization | Rollback | Security | Telemetry | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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