Edge AI Engineer
Tasks
- Apply model quantization pruning distillation
- Build edge inference runtimes
- Build privacy preserving telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites for accuracy latency energy
- Drive responsible AI privacy and bias evaluation
- Implement on device model update versioning rollback
- Implement secure execution model protection integrity verification
- Maintain technical documentation runbooks and operational procedures
- Optimize accelerator backend performance
- Tune latency energy efficiency and memory footprint
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C++ | Core ML | DSPs | Edge Computing | Embedded Systems | Hybrid Cloud | Integrity verification | Knowledge Distillation | Machine Learning | Mobile SoCs | Model Compression | Model updates | Model versioning | NPUs | ONNX Runtime | Privacy | Pruning | Python | Quantization | Rollback | Security | Telemetry | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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