Edge AI Engineer
Tasks
- Apply quantization pruning distillation and architectural optimization
- Build cross platform inference runtimes
- Build telemetry pipelines with privacy controls
- Collaborate with hardware, firmware, and product teams
- Design and implement edge AI solutions optimized for hardware
- Design hybrid edge cloud architectures
- Develop benchmarking suites for accuracy latency and energy
- Drive responsible AI for privacy and bias evaluation
- Implement on device model update versioning and rollback
- Implement secure execution paths model protection and integrity verification
- Maintain technical documentation and operational runbooks
- Optimize models for accelerator backends
- Stay current with edge AI hardware and software releases
- Tune model performance for latency energy and memory
Perks/Benefits
- 100 percent remote work
- Career growth potential
- Direct W2 employment
- H1B transfer support
- Long term multi year engagement
Skills/Tech-stack
Benchmarking | Bias Evaluation | C++ | Core ML | DSPs | Embedded Systems | Energy Efficiency | Federated Learning | Hardware acceleration | Inference Runtime | Integrity verification | Knowledge Distillation | Latency optimization | Machine Learning | Memory Optimization | Mobile SoCs | Model Compression | Model Optimization | NPUs | ONNX Runtime | On Device Privacy | Pruning | Python | Quantization | Security | Telemetry pipelines | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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