Edge AI Engineer
Tasks
- Apply model compression techniques
- Build edge inference runtimes
- 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 energy trade offs
- Evaluate responsible AI with on device privacy and bias
- Implement on device model update workflows
- Maintain technical documentation and runbooks
- Optimize model performance for latency and energy
- Optimize models for hardware accelerators
- Secure model execution and integrity verification
Perks/Benefits
Skills/Tech-stack
Benchmarking | C++ | Cloud architectures | Core ML | Cross-platform | Cross-platform development | DSPs | Edge Computing | Edge/cloud architectures | Efficiency optimization | Embedded Systems | Energy Efficiency | Energy efficiency optimization | Hybrid Edge Cloud | Hybrid Edge Cloud Architectures | Inference Optimization | Integrity verification | Knowledge Distillation | Latency optimization | Machine Learning | Memory Optimization | Mobile GPUs | Mobile SoCs | Model Compression | Model Pruning | Model Quantization | Model Security | NPUs | ONNX Runtime | On Device Privacy | Performance Profiling | Platform Development | Python | Telemetry pipelines | TensorFlow Lite
Roles
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