Promotional Materials Editorial Lead — MLR & FDA Submissions

What You'll Do

• Port GPU-based video analytics models (object detection, classification) to CPU-only router targets
• Optimize inference pipeline to stay under 100MB memory footprint using SLMs
• Build containerized architecture with dynamic cloud-driven model loading
• Tune accuracy/performance tradeoffs on ARM/MIPS router hardware
• Integrate with Cradlepoint OS and PrplOS environments
• Benchmark and iterate on detection accuracy vs. latency on constrained hardware
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Required

• 4+ years in embedded systems or edge ML deployment
• Experience with containerization (Docker, LXC) on constrained devices
• ML model optimization: quantization, pruning, ONNX, TensorFlow Lite, OpenVINO
• Video analytics / computer vision (YOLO variants, object detection pipelines)
• Python + C/C++ on Linux embedded targets
• Cross-compilation, profiling, and memory optimization
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Strong Plus

• Cradlepoint NetCloud / PrplOS / OpenWRT experience
• NPU/DSP acceleration on router-class SoCs
• DeepStream or similar inference pipeline experience (GPU→CPU migration)
• SLM deployment (sub-1B parameter models on edge)
• RTSP/video streaming on embedded Linux
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You Are

• Comfortable with no GPU — CPU-only inference is the constraint, not a fallback
• Pragmatic about accuracy tradeoffs at the edge
• Experienced navigating vendor OS lock-in and limited debugging toolchains

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