Embedded AI Engineer
Tasks
- Apply quantization pruning distillation and optimization
- Build inference runtimes
- Build privacy aware telemetry pipelines
- Collaborate with hardware, firmware, and product teams
- Design Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Drive responsible AI privacy and bias evaluation
- Implement on device model update versioning and rollback
- Implement secure execution and integrity verification
- Maintain technical documentation
- Optimize models for accelerator backends
- Tune latency energy efficiency and memory footprint
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Core ML | DSP | Edge AI | Energy Efficiency | Federated Learning | Hybrid Architecture | Inference Optimization | Knowledge Distillation | Latency optimization | Machine Learning | Mobile GPU | Model Benchmarking | Model Compression | Model versioning | NPU | ONNX Runtime | Privacy | Profiling | Pruning | Python | Quantization | Rollback | TensorFlow Lite
Education
N/A
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