Lead Editor

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 --- 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 --- 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 --- 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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