Hi I hope you're doing well. We have a position as an " Embedded ML Engineer " with our client. Please find the details below, and if interested, please send me your updated resume in Word format Role

• Embedded ML Engineer Location
• Irving, TX
• onsite Role Port and optimize a containerized video analytics pipeline to run on CPU-constrained router hardware (Cradlepoint OS, Wi-Fi 7 PrplOS, FWA routers). You'll own the full stack: model optimization, container architecture, and on-device inference performance.
• 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