Edge AI Engineer
Tasks
- Apply quantization and model compression techniques
- Build cross platform inference runtimes
- Build telemetry pipelines for privacy compliant continuous improvement
- Collaborate with hardware, firmware, and product teams
- Design and implement edge AI solutions optimized for mobile and embedded hardware
- Design hybrid edge cloud architectures for connectivity degradation
- Develop benchmarking suites for accuracy latency and energy trade offs
- Evaluate responsible AI including on device privacy and bias
- Implement on device model update versioning and rollback workflows
- Implement secure execution model protection and 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 efficiency and memory
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Core ML | DSP | Edge Cloud | Edge Computing | Embedded Systems | Energy Efficiency | Federated Learning | Hybrid Edge Cloud | Knowledge Distillation | Latency optimization | Machine Learning | Memory Optimization | Mobile GPUs | Model Benchmarking | Model Compression | NPU | ONNX Runtime | On-device Inference | Privacy | Pruning | Python | Quantization | Security | Telemetry pipelines | TensorFlow Lite
Education
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