Edge AI Engineer
Tasks
- Apply quantization and pruning
- Build inference runtimes
- Build privacy preserving telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Deploy ML models to edge devices
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Evaluate bias and on device privacy
- Implement on device model update and rollback
- Implement secure model execution and integrity verification
- Maintain technical documentation and runbooks
- Optimize model latency and energy efficiency
- Optimize models for NPUs and mobile GPUs
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C++ | Core ML | Edge inference | Efficiency optimization | Energy Efficiency | Energy efficiency optimization | Federated Learning | Knowledge Distillation | Latency optimization | Machine Learning | Model Compression | Model Update | Model Update Versioning | Model profiling | ONNX Runtime | Privacy | Pruning | Python | Quantization | Security | Telemetry pipelines | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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