Hyeonki Hong - Product Team Lead at Moreh

Hyeonki Hong

Product Team Lead at Moreh

South Korea

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Summary

👤

Senior

🎓

Top School

Hyeonki Hong is a product-focused engineering leader with 8 years of experience building backend systems, distributed inference pipelines, and production ops across embedded and cloud environments. Currently Product Team Lead at Moreh, he progressed from hands-on DevOps to leading product efforts, blending infrastructure reliability with product thinking. His background spans embedded Linux, microcontroller firmware, and edge ML optimization (TPU/Jetson), reflecting a rare full-stack embedded-to-cloud skill set. Open-source contributions include adding ODROID support to Adafruit Blinka and practical YOLOv4 work on TensorFlow/TFLite, showing both low-level board integration and applied ML expertise. Academically strong with an M.S. in AI Systems and high academic honors, he pairs rigorous research-led thinking with startup pragmatism from co-founding product efforts. Colleagues describe him as an engineer who ships end-to-end solutions while still diving into pin mappings and loss functions when needed.

8 years of coding experience

6 years of employment as a software developer

석사, 인공지능시스템, 4.35/4.5, 석사, 인공지능시스템, 4.35/4.5 at Hanyang University

Github Skills (21)

bsp10

develop10

python10

hardware-interface10

integrations10

gpio10

tensorflow10

sys10

system-integration10

micropython10

packager10

object-detection10

computer-vision10

embedded10

yolov410

Programming languages (15)

SmartyC++JinjaCMakefileGoMustacheHTML

Github contributions (5)

hunglc007/tensorflow-yolov4-tflite

Jun 2020 - Jul 2020

YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite

Role in this project:

ML Engineer

Contributions:9 commits, 5 PRs, 18 comments in 24 days

Contributions summary:Hyeonki primarily contributed to the core of the YOLOv4 implementation, focusing on dataset processing, model training, and loss calculation. Their work involved modifications across multiple Python files, including `core/dataset.py`, `core/utils.py`, and `core/yolov4.py`. The commits show an understanding of image preprocessing, bounding box handling, and the overall architecture of the YOLOv4 model. The user also applied code formatting using pylint.

tensorflow-2tensorflow-2-0tinyweightsyolov4-tiny

adafruit/Adafruit_Blinka

Mar 2020 - May 2020

Add CircuitPython hardware API and libraries to MicroPython & CPython devices

Role in this project:

Embedded Systems Engineer / IoT Developer

Contributions:11 commits, 6 PRs, 10 comments in 2 months

Contributions summary:Hyeonki primarily contributed to the `adafruit_blinka` repository by adding support for new hardware platforms, specifically the Amlogic S905X3 and the Hardkernel Odroid C4. These contributions involved creating or modifying pin definitions and board-specific configurations to integrate these devices with the existing CircuitPython hardware API. Further work included updating the necessary busio and board configurations.

apipythonhardware-apihardwarecircuitpython

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