Embedded AI Engineer
Tasks
- Apply quantization and pruning
- Build edge inference runtimes
- Build privacy preserving telemetry pipelines
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Drive responsible AI bias evaluation and privacy
- Implement on device model update versioning and rollback
- Implement secure execution paths and model integrity verification
- Maintain technical documentation and runbooks
- Optimize latency energy efficiency and memory
- Optimize models for NPUs and accelerators
- Translate edge AI hardware and software updates into adoption recommendations
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Core ML | DSP | Edge inference | Embedded Systems | Energy Efficiency | Federated Learning | GPU | Knowledge Distillation | Latency optimization | Machine Learning | Memory Optimization | Mobile SoC | Model Compression | Model Optimization | NPU | ONNX Runtime | Performance Engineering | Privacy & Security | Profiling | Pruning | Python | Quantization | Telemetry | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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