Helin Wang
Staff Software Engineer Tech Lead Manager at Google
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHelin Wang is a Staff Software Engineer and tech lead manager with 11 years of experience building scalable ML and MLOps systems, currently leading Google’s Structured Data Classification/Regression team in Sunnyvale. She spearheaded the design and launch of Tabular Workflows on Vertex AI, coordinating cross-org efforts with 50+ engineers to bring production-ready AutoML for tabular data to market. Her background spans core open-source deep learning work on PaddlePaddle—contributing distributed training, CUDA build fixes, and deployment examples—and hands-on backend engineering for Google Cloud client libraries. Helin combines systems-level expertise (distributed training, Kubernetes, CUDA) with product delivery and team leadership, moving models from research prototypes to enterprise-grade services. An early tinkerer who sold and maintained game-hack software as an undergraduate, she brings a practical, security-aware mindset to shipping robust, real-world systems.
11 years of coding experience
11 years of employment as a software developer
Bachelor, Mechanical Engineering, Bachelor, Mechanical Engineering at University of Science and Technology of ChinaMaster, Mechanical Engineering, Master, Mechanical Engineering at Carnegie Mellon University
Github Skills (24)
distributed-training10
back-end-development10
python10
pandas10
machine-learning10
deep-learning10
paddlepaddle10
api-design9
continuous-deployment9
ml-deployment9
apidoc9
neural-network9
api9
cuda8
mnist8
Programming languages (15)
Github contributions (5)
PaddlePaddle/book
Mar 2017 - Feb 2018
Deep Learning 101 with PaddlePaddle （『飞桨』深度学习框架入门教程）
Role in this project:
ML Engineer
ML EngineerContributions:45 commits, 53 PRs, 28 pushes in 1 year
Contributions summary:Helin primarily focused on modifying and adding examples within the PaddlePaddle book repository, specifically involving deep learning models and related tools. The contributions included implementing and fixing code for Word2Vec, image classification using VGG and ResNet models, and recommender systems, all using the PaddlePaddle framework. Furthermore, the user added a Paddle Serve example, alongside a client example for MNIST digit recognition, demonstrating deployment and inference capabilities.
deep-learningpytorchpaddlepaddletensorflow
PaddlePaddle/Paddle
Dec 2017 - May 2018
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Role in this project:Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:55 commits, 323 PRs, 131 pushes in 4 months
Contributions summary:Helin contributed to the core functionalities of the PaddlePaddle deep learning framework. They fixed build issues related to CUDA integration and refactored code, including scaffolding for a new Fluid API. The user also added and updated examples, showcasing integration with the new API and enabling distributed training. Furthermore, they improved comments and the API for the Trainer and Inferencer, and improved overall project comments.
pytorchpythonparalleldeep-learningpaddlepaddle
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