Edge AI Engineer
Tasks
- Apply quantization and pruning to fit edge constraints
- Build cross platform inference runtimes
- Build telemetry pipelines with privacy controls
- Collaborate with hardware, firmware, and product teams
- Design and implement Edge AI solutions
- Design hybrid edge cloud architectures
- Develop benchmarking suites for accuracy latency and energy
- Evaluate bias and privacy for responsible AI
- Implement on device model update versioning and rollback
- Implement secure execution paths and integrity verification
- Maintain technical documentation and runbooks
- Optimize model performance for latency energy efficiency and memory
- Optimize models for accelerator backends
- Stay current with edge AI hardware and software releases and recommend adoption
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Core ML | DSPs | Edge inference | Embedded accelerators | Energy Efficiency | Federated Learning | Integrity verification | Knowledge Distillation | Latency optimization | Machine Learning | Memory Optimization | Mobile GPUs | Mobile SoC | Model Benchmarking | Model Compression | NPUs | ONNX | ONNX Runtime | Privacy Preserving Telemetry | Privacy-preserving | Pruning | Python | Quantization | Secure execution | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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