W2 ONLY - Need Senior Software Engineer C++ / Irving, TX

ReqRoute,Inc; Irving (TX); On-site; USD 120,000 - 190,000; Full time

Job summary

ReqRoute,Inc. is seeking a Senior Software Engineer to port and optimize a containerized video analytics pipeline for CPU-only router hardware.

You will own the full stack—from model optimization to on-device inference performance—and integrate with Cradlepoint OS and PrplOS environments. The role emphasizes deploying quantization/pruning, managing memory to under 100MB, and achieving favorable accuracy-latency balances on ARM/MIPS platforms.

Qualifications

• 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).

Responsibilities

• Port GPU-based video analytics models 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.

Skills

C++

Python

Linux Embedded

Edge ML deployment

Tools

Docker

LXC

ONNX

TensorFlow Lite

OpenVINO

Job description

Title: Senior Software Engineer
Skills: C++
Location: Irving, TX

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