Edge AI Engineer
Tasks
- Apply quantization and pruning
- Build cross platform inference runtimes
- Build privacy preserving telemetry pipelines
- Create technical documentation and runbooks
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Evaluate responsible AI bias and privacy
- Implement on device model update and rollback
- Maintain edge AI hardware and software recommendations
- Optimize model latency and energy efficiency
- Optimize models for accelerator backends
- Secure model execution on edge devices
Perks/Benefits
Skills/Tech-stack
Architecture Optimization | Benchmarking | C++ | Cloud architectures | Core ML | DSP | Edge Cloud | Edge Computing | Edge/cloud architectures | Embedded Systems | Federated Learning | Hybrid architectures | Knowledge Distillation | Machine Learning | Mobile GPUs | Model Compression | Model versioning | NPU | Neural Network | Neural Network Architecture | Neural Network Architecture Optimization | ONNX | ONNX Runtime | Performance Profiling | Privacy | Pruning | Python | Quantization | Rollback | Security | Telemetry | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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