Estella Xin
Software Engineer at Weights & Biases
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolEstella Xin is a software engineer with 10 years of experience building scalable backend systems and ML workflows, currently contributing full-stack to the Machine Learning Workflow team at Weights & Biases in New York. She has deep experience in artifact management and storage optimizations—adding artifact timeouts, reference handling, S3 multipart uploads, and resolving circular metadata reference bugs in the widely used wandb platform. Prior roles include real-time bond pricing infrastructure at Bloomberg and backend Golang work at Uber, plus data engineering and full-stack internships that shaped a pragmatic, performance-minded approach. A Turing Scholar with a concentration in machine learning from UT Austin, she blends production-grade engineering with ML infrastructure know-how and a knack for shipping robust, scalable systems.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Science (BS), Computer Science (Turing Scholar Honors Program), Concentration in Machine Learning, Bachelor of Science (BS), Computer Science (Turing Scholar Honors Program), Concentration in Machine Learning at The University of Texas at AustinTexas Academy of Math and Science, Texas Academy of Math and Science at University of North Texas
English, Chinese
Github Skills (21)
artifact10
python10
artifactory10
artifacts10
mlops10
versioning10
rep9
amazon-s39
repr9
aws-s39
s3-bucket9
experiment9
tensorflow8
machine-learning8
pytorch8
Programming languages (3)
Github contributions (5)
wandb/wandb
Aug 2022 - Dec 2022
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Role in this project:
Back-end & MLOps Engineer
Back-end & MLOps EngineerContributions:1 release, 194 reviews, 10 commits in 3 months
Contributions summary:Estella primarily contributed to the development and improvement of artifact management features within the Weights & Biases platform. They added functionality for artifact timeouts and reference handling, optimizing the upload process. They also addressed a circular reference error related to metadata updates and implemented S3 multipart uploading, improving the platform's scalability and performance. Their work touched both the back-end infrastructure and tools related to artifact lifecycle and storage.
pythoncollaborationtensorflowhyperparameter-tuningcli
estellazx/estellazx.github.io
May 2016 - Dec 2016
Contributions:12 commits, 11 pushes, 1 branch in 7 months
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