Embedded AI Engineer
Tasks
- Build benchmarking suites
- Build edge inference runtimes
- Build telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Conduct privacy and bias evaluation
- Deploy machine learning models
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Implement on device model update and rollback
- Implement quantization, pruning, distillation
- Implement secure execution paths
- Maintain technical documentation
- Optimize for accelerator backends
- Optimize models for latency energy and memory
- Translate edge AI advances into recommendations
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Core ML | Edge AI | Energy Efficiency | Federated Learning | Footprint Optimization | Hardware acceleration | Inference engine | Knowledge Distillation | Latency optimization | Machine Learning | Memory Footprint | Memory Footprint Optimization | Model Compression | Model Optimization | Model Security | ONNX Runtime | Performance Engineering | Privacy-preserving | Privacy-preserving machine learning | Profiling | Pruning | Python | Quantization | Telemetry pipelines | TensorFlow Lite
Education
Roles
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