Embedded ML Engineer

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.

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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

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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

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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