Edge AI Engineer
Tasks
- Apply quantization pruning distillation and architectural optimization
- Build cross platform inference runtimes
- Build telemetry pipelines with privacy protections
- Collaborate with hardware, firmware, and product teams
- Design and implement edge AI solutions optimized for mobile and embedded hardware
- Design hybrid edge cloud architectures
- Develop benchmarking suites for accuracy latency and energy
- Drive responsible AI privacy and bias evaluation
- Implement on device model update and rollback workflows
- Implement secure execution and model integrity verification
- Maintain technical documentation and operational runbooks
- Optimize models for accelerator backends
- Stay current with edge AI hardware and software updates
- Tune model performance for latency energy and memory
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C plus plus | Core ML | Device security | Edge inference | Energy optimization | Federated Learning | Hardware-Aware Optimization | Hybrid Cloud | Knowledge Distillation | Latency optimization | Memory Optimization | Model Compression | Model Deployment | ONNX Runtime | On Device Privacy | On-device security | Profiling | Pruning | Python | Quantization | Secure execution | Telemetry pipelines | TensorFlow Lite
Education
Roles
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