Edge AI Engineer
Tasks
- Apply model compression and quantization
- Build cross platform inference runtimes
- Build privacy preserving telemetry pipelines
- Collaborate with hardware and firmware teams
- Create technical documentation and runbooks
- Deploy models to mobile and embedded platforms
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Evaluate responsible AI including privacy and bias
- Implement on device model update and rollback
- Implement pruning and distillation
- Implement secure model execution paths
- Optimize inference latency and energy
- Tune performance for target accelerators
- Verify model integrity on edge devices
Perks/Benefits
- N/A
Skills/Tech-stack
Bias Evaluation | C++ | Core ML | DSP | Edge Cloud | Edge inference | Embedded Systems | Federated Learning | Hybrid Edge Cloud | Integrity verification | Knowledge Distillation | Machine Learning | Mobile GPUs | Model Compression | Model Optimization | Model Update | NPU | ONNX Runtime | On Device Model Update | Performance Profiling | Privacy Preservation | Pruning | Python | Quantization | Secure execution | Telemetry pipelines | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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