{"schemaVersion":"jobsearcher.job.v1","id":"0543c2f23d0a0ad5084d0815","url":"https://jobsearcher.com/jobs/0543c2f23d0a0ad5084d0815","canonicalUrl":"https://jobsearcher.com/jobs/0543c2f23d0a0ad5084d0815","title":"Linux embedded (Python + C/C++)","description":"Job Title: Linux embedded (Python + C/C++)Location: Irving, TX(Onsite)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 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","company":"Smart IT Frame","rawCompany":"smart it frame","city":"Arlington","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-09-26T07:22:54.484Z","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":"Linux embedded (Python + C/C++)","description":"Job Title: Linux embedded (Python + C/C++)Location: Irving, TX(Onsite)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 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","datePosted":"2026-09-26T07:22:54.484Z","dateModified":"2026-09-26T07:22:54.484Z","hiringOrganization":{"@type":"Organization","name":"Smart IT Frame","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Arlington","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"0543c2f23d0a0ad5084d0815"},"url":"https://jobsearcher.com/jobs/0543c2f23d0a0ad5084d0815"}}