Are you looking for an Embedded Systems Engineer with ML skills like Zack? Zack is a husband and father of two, seeking new opportunities to apply his expertise. He has a strong background in building real-time systems from the ground up, including edge device deployments, facial recognition, and deep learning audio denoising.
Raspberry Pi Facial Recognition System
Zack developed a facial recognition system on Raspberry Pi networks with real-time MJPEG video streaming. This system includes automated network detection and was built using Python with TCP/IP socketing. It leverages OpenCV and TensorFlow for efficient performance.
Deep Learning Audio Denoising
Zack also created an audio denoising system based on custom U-Net architectures. This system utilizes multi-loss functions and cross-validation on the VoiceBank-DEMAND dataset for high-quality results. The deep learning approach ensures effective noise reduction in audio signals.
Full-stack Data Pipelines
Zack built data pipelines with GUI interfaces to support RF emitter diagnostics on distributed Raspberry Pi networks. This involved utilizing C++, QML, and JavaScript to streamline the process and enhance diagnostic capabilities.
Hand Pose Classifier
Another impressive project by Zack is the real-time hand pose classifier with over 99% accuracy at over 30 FPS. This classifier is designed for gesture recognition and showcases Zack’s commitment to precision and performance in his work.
IMU Calibration and Hazard Light Systems
Zack automated IMU calibration and developed algorithms for embedded hazard light systems using Python. These systems were designed for military-grade RF mobile emitters, demonstrating Zack’s versatility in tackling complex engineering challenges.
Zack’s diverse skill set and hands-on experience make him a valuable asset in roles involving embedded ML systems. If you have any opportunities or insights to share with Zack, feel free to connect with him. Let’s support the tech community and help professionals like Zack find their next exciting challenge!
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