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# Embedded Computer Vision Engineer

- **Company:** TechDigital Corporation
- **Location:** United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Computer Vision, C++ (Programming Language), Program Optimization, Profiling, Software Debugging, Linux on Embedded Systems, Python (Programming Language), Machine Learning, Object Detection, Tensorflow, Openwrt, RTSP, Containerization, ONNX (Open Neural Network Exchange) Format, Video Streaming, Lxc, Docker
- **Published:** September 15, 2026
- **Apply:** https://www.dice.com/job-detail/9f72bc14-7e61-4825-9d40-1d19db786566

## About the Role

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
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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
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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

## Description

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


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