Owen Wang
Engineering at Fay Nutrition
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolOwen Wang is a seasoned full-stack engineer and engineering leader based in San Francisco with over a decade of experience building and scaling product and infrastructure across startups and large companies. He’s led hiring, team design, and re-architecture efforts that moved companies from monoliths to microservices while growing revenue and keeping team morale through hard times. Hands-on across backend, frontend, mobile, IoT, and test automation, he’s been an early engineer at YC-backed startups and has operated at scale at Google, bringing both startup agility and platform rigor. Owen contributes to computer vision tooling—improving data loading, keypoint handling, and custom dataset support in the popular Detectron2 project—reflecting a practical interest in ML systems. Currently investigating how software can drive social impact, he’s focused on products that nudge healthier eating and measurable behavior change.
9 years of coding experience
10 years of employment as a software developer
Part-Time Non-Degree, Part-Time Non-Degree at North Carolina State UniversityB.A.Sc Computer Engineering, B.A.Sc Computer Engineering at University of Waterloo
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
data-preprocessing10
computer-vision10
pytorch10
machine-learning10
data-loading10
dataprep10
deep-learning10
preprocessing10
detectron10
python10
preprocess10
modeling9
trainings9
segmentation9
image-segmentation9
Programming languages (2)
Github contributions (5)
facebookresearch/detectron2
Oct 2019 - Mar 2022
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Role in this project:
ML Engineer
ML EngineerContributions:5 commits in 2 years 5 months
Contributions summary:Owen's contributions primarily revolve around enhancing the functionality and usability of the Detectron2 platform for computer vision tasks. Their work includes adding configuration options to control data loading, such as filtering unannotated images, and modifying the keypoint ROI flow to handle hard negatives effectively. Furthermore, the user added features to support custom file handling and dataset weighting, making the platform more adaptable for diverse project needs and specific datasets.
detectronobject-detectionsegmentationvisual-recognition
Maninae/segmentation-191
Feb 2018 - Mar 2018
Contributions:40 commits, 11 pushes in 1 month
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