{"schemaVersion":"jobsearcher.job.v1","id":"58fb12f3eb8ee92670d6799c","url":"https://jobsearcher.com/jobs/58fb12f3eb8ee92670d6799c","canonicalUrl":"https://jobsearcher.com/jobs/58fb12f3eb8ee92670d6799c","title":"Embedded Linux + C/C++ + Python","description":"Title: Embedded Linux + C/C++ + PythonLocation: Irving, TXSkills: C++RolePort 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 DoPort GPU-based video analytics models (object detection, classification) to CPU-only router targetsOptimize inference pipeline to stay under 100MB memory footprint using SLMsBuild containerized architecture with dynamic cloud-driven model loadingTune accuracy/performance tradeoffs on ARM/MIPS router hardwareIntegrate with Cradlepoint OS and PrplOS environmentsBenchmark and iterate on detection accuracy vs. latency on constrained hardwareRequired4+ years in embedded systems or edge ML deploymentExperience with containerization (Docker, LXC) on constrained devicesML model optimization: quantization, pruning, ONNX, TensorFlow Lite, OpenVINOVideo analytics / computer vision (YOLO variants, object detection pipelines)Python + C/C++ on Linux embedded targetsCross-compilation, profiling, and memory optimizationStrong PlusCradlepoint NetCloud / PrplOS / OpenWRT experienceNPU/DSP acceleration on router-class SoCsDeepStream or similar inference pipeline experience (GPU→CPU migration)SLM deployment (sub-1B parameter models on edge)RTSP/video streaming on embedded LinuxYou AreComfortable with no GPU — CPU-only inference is the constraint, not a fallbackPragmatic about accuracy tradeoffs at the edgeExperienced navigating vendor OS lock-in and limited debugging toolchains","company":"New York Technology Partners","rawCompany":"new york technology partners","city":"Arlington","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-09-17T08:08:04.061Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.00","title":"Computer Occupations, All Other","slug":"computer-occupations-all-other"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Embedded Linux + C/C++ + Python","description":"Title: Embedded Linux + C/C++ + PythonLocation: Irving, TXSkills: C++RolePort 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 DoPort GPU-based video analytics models (object detection, classification) to CPU-only router targetsOptimize inference pipeline to stay under 100MB memory footprint using SLMsBuild containerized architecture with dynamic cloud-driven model loadingTune accuracy/performance tradeoffs on ARM/MIPS router hardwareIntegrate with Cradlepoint OS and PrplOS environmentsBenchmark and iterate on detection accuracy vs. latency on constrained hardwareRequired4+ years in embedded systems or edge ML deploymentExperience with containerization (Docker, LXC) on constrained devicesML model optimization: quantization, pruning, ONNX, TensorFlow Lite, OpenVINOVideo analytics / computer vision (YOLO variants, object detection pipelines)Python + C/C++ on Linux embedded targetsCross-compilation, profiling, and memory optimizationStrong PlusCradlepoint NetCloud / PrplOS / OpenWRT experienceNPU/DSP acceleration on router-class SoCsDeepStream or similar inference pipeline experience (GPU→CPU migration)SLM deployment (sub-1B parameter models on edge)RTSP/video streaming on embedded LinuxYou AreComfortable with no GPU — CPU-only inference is the constraint, not a fallbackPragmatic about accuracy tradeoffs at the edgeExperienced navigating vendor OS lock-in and limited debugging toolchains","datePosted":"2026-09-17T08:08:04.061Z","dateModified":"2026-09-17T08:08:04.061Z","hiringOrganization":{"@type":"Organization","name":"New York Technology Partners","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Arlington","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"58fb12f3eb8ee92670d6799c"},"url":"https://jobsearcher.com/jobs/58fb12f3eb8ee92670d6799c"}}