Winnie Xu
Software Engineer (Keep) at Google
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolWinnie Xu is a software engineer with eight years of experience building customer-facing and infrastructure systems at Google, spanning Ads, Search, and Collaboration. She architects and implements solutions across the stack—Java backends, AngularDart and Flutter UIs, and SQL—while routinely leading cross-functional efforts with UX, PMs, and tech writers. Her background includes owning high-impact rewrites for Ad Manager traffic hotspots and shipping experimental mobile and web food exploration features. An active open-source contributor, Winnie implemented an OCI image puller and caching utilities for the widely used rules_docker Bazel project, enabling container image downloads without a Docker client. A UofT/UCSD-trained engineer with experience at research-focused orgs and startups, she combines product sensitivity with systems-level rigor and a knack for turning research ideas into production-ready tools.
8 years of coding experience
9 years of employment as a software developer
University of California San DiegoHigh School, High School at American High School
English, Chinese
Stackoverflow
31reputation
558reached
1answer
0questions
Github Skills (15)
docker10
container10
containerd10
go10
containerization10
bazel10
dockers10
cicd9
starlark9
cloud-computing8
keras6
deep-learning6
tpu6
tensorflow6
google-colaboratory6
Programming languages (7)
Github contributions (5)
bazelbuild/rules_docker
Jun 2019 - Aug 2019
Rules for building and handling Docker images with Bazel
Role in this project:
Backend & DevOps Engineer
Backend & DevOps EngineerContributions:20 commits, 59 PRs, 55 comments in 2 months
Contributions summary:Winnie primarily contributed to the implementation of a new image puller binary and associated utilities for the rules_docker project. Their work involved developing an OCI image puller and writer utility and integrating it with existing Bazel rules, enabling the download of container images without requiring a Docker client. They also implemented features to support different image formats (Docker tarball and OCI layout) and added caching capabilities. Furthermore, they updated existing Starlark rules to utilize the new puller and loader binaries and made enhancements to accommodate file structure changes.
docker-imagebazelgooglestarlarkdocker
xwinxu/bayeSDE
Feb 2021 - Feb 2022
Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
Contributions:42 commits, 3 PRs, 28 pushes in 1 year
pytorchstochastic-differential-equationsequationsbayesian-layersdeep-learning
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