Edge AI Engineer
Tasks
- Apply quantization pruning distillation and compression techniques
- Build inference runtimes with edge frameworks
- Build privacy preserving telemetry pipelines
- Design edge AI solutions optimized for hardware
- Design hybrid edge cloud architectures
- Develop benchmarking suites for accuracy latency and energy
- Evaluate bias and privacy for responsible AI
- Implement on device model update versioning and rollback
- Implement secure execution paths and model integrity verification
- Maintain technical documentation and operational runbooks
- Optimize models for accelerator backends
- Translate edge AI hardware and software updates into adoption recommendations
- Tune model performance for latency energy efficiency and memory
Perks/Benefits
Skills/Tech-stack
Benchmarking | Bias Evaluation | C++ | Core ML | Embedded Systems | Federated Learning | Hardware Architecture | Knowledge Distillation | Machine Learning | Mobile Hardware | Mobile Hardware Architecture | Model Compression | Model Deployment | ONNX Runtime | On Device Personalization | Performance Profiling | Privacy | Pruning | Python | Quantization | Security | Telemetry | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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