Embedded Machine Learning Engineer
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.
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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
