Embedded Computer Vision Engineer

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

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