Jing Huang
Engineering Manager at Meta
Menlo Park, California, United States
Summary
👤
Senior🎓
Top SchoolJing Huang is an engineering manager in Menlo Park with a decade of experience building web services and distributed systems across Meta, AWS, Qualcomm, and Broadcom. She leads product-focused engineering teams at Meta, with a background in groups integrity and community governance as well as large-scale data services like EMR. Jing holds a Ph.D. in Electrical and Computer Engineering from UC Irvine and is a prolific researcher with 30 published papers and over 2,000 citations, bringing rigorous academic depth to product engineering. She contributes to high-profile open-source work in computer vision—enhancing rotated object detection and evaluation tooling in Facebook Research’s Detectron2—demonstrating strong applied ML and systems skills. Known for translating complex research into production-grade features, she blends algorithmic rigor with pragmatic engineering and operational reliability. Colleagues describe her as a technical leader who balances mentorship, governance, and hands-on contributions to hard systems problems.
10 years of coding experience
8 years of employment as a software developer
Ph.D., Electrical and Computer Engineering, 4.0/4.0, Ph.D., Electrical and Computer Engineering, 4.0/4.0 at University of California, Irvine
Github Skills (6)
object-detection10
computer-vision10
pytorch10
machine-learning10
python10
caffe9
Programming languages (5)
Github contributions (5)
facebookresearch/detectron2
Oct 2019 - Oct 2020
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 2 PRs, 6 comments in 1 year
Contributions summary:Jing primarily contributed to the development and enhancement of rotated object detection capabilities within the Detectron2 framework. Their work involved enabling inferencing and visualization for rotated models, implementing XYWHA to XYXY box conversions, and fixing issues related to numerical instability and incorrect IoU calculations specific to rotated boxes. The user also developed a universal COCO-style evaluation framework for rotated object detection, demonstrating a strong understanding of computer vision and model evaluation techniques. Furthermore, they addressed critical runtime errors and supported the model conversion to Caffe2.
detectron2deep-learningface-recognitionrecognitionobject-detection
SuperIRabbit/pytorch
Dec 2018 - Jan 2020
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:72 pushes, 7 branches in 1 year 1 month
pythongpu-accelerationdeep-learninggpuacceleration
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