Edge AI Engineer
Tasks
- Apply quantization pruning distillation
- Build cross platform inference runtimes
- Build privacy preserving telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Deploy machine learning models on edge devices
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Drive responsible AI privacy and bias evaluation
- Implement on device model updates versioning rollback
- Implement secure execution paths model protection integrity verification
- Maintain technical documentation
- Optimize models for accelerator backends
- Optimize models for latency energy and memory
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C++ | Core ML | Edge Computing | Embedded accelerators | Energy Efficiency | Federated Learning | Knowledge Distillation | Latency optimization | Machine Learning | Mobile GPUs | Mobile SoCs | Model Compression | Model Integrity | Model Optimization | NPUs | ONNX Runtime | Performance Profiling | Privacy Preservation | Pruning | Python | Quantization | Security | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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