Edge AI Engineer
Tasks
- Apply model compression and quantization
- Build inference runtimes for cross platform deployment
- 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 and energy
- Evaluate bias and privacy for responsible AI
- Implement on device model update versioning and rollback
- Implement secure execution paths and model integrity verification
- Maintain technical documentation and runbooks
- Optimize models for hardware accelerator backends
- Stay current with edge AI hardware and software updates
- Tune model performance for latency energy efficiency and memory
Perks/Benefits
Skills/Tech-stack
Benchmarking | Bias Evaluation | C++ | Core ML | DSPs | Embedded Systems | Federated Learning | Hardware acceleration | Inference | Knowledge Distillation | Machine Learning | Mobile GPUs | Mobile SoCs | Model Compression | Model Optimization | NPUs | ONNX Runtime | On Device Privacy | Performance Profiling | Pruning | Python | Quantization | Secure execution | Security | Telemetry | TensorFlow Lite
Education
Roles
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