Ann Zhang
Senior Software Engineer, Atlas at Stripe
New York City Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolAnn Zhang is a software engineer at Stripe with a strong foundation in cloud-native backends, full-stack development, and machine learning tooling, built on an MEng and BS in Computer Science from MIT. She brings a unique decade-long background in market entry and sustainability-focused strategy across nonprofit and commercial sectors, which informs her product-minded approach to engineering. Her open-source contributions to MLflow improved tracking and evaluation APIs, reflecting hands-on experience with production ML lifecycle tooling. Prior roles at Salesforce and research stints at MIT Senseable City Lab show a track record of shipping data pipelines, sensor-driven analytics, and payments monitoring systems. Comfortable iterating between research, product, and engineering, she aims to leverage technology and networks to drive measurable social and environmental impact.
3 years of coding experience
4 years of employment as a software developer
Master of Engineering - MEng Computer Science, Master of Engineering - MEng Computer Science at Massachusetts Institute of TechnologyHigh School Diploma, High School Diploma at Great Neck South High School
Github Skills (8)
machine-learning10
mlflow10
python10
data-analysis9
apidoc9
model-management9
api9
testing8
Programming languages (3)
Github contributions (5)
mlflow/mlflow
May 2023 - Aug 2025
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
Role in this project:
Back-end & ML Engineer
Back-end & ML EngineerContributions:187 reviews, 187 PRs, 60 pushes in 2 years 4 months
Contributions summary:Ann primarily contributed to the implementation of a new `get_parent_run` fluent API, enhancing the functionality of the MLflow tracking client. Their work included defining the API, integrating it with the client API, and creating corresponding unit tests. Additionally, the user made adjustments to support list[str] inputs within the `_enforce_schema` function, and added metrics to the `mlflow.evaluate` API, thus impacting model evaluation capabilities. They addressed code examples, documentation and general improvements to multiple aspects of the project.
aimlflowmlmodelmachine-learningml
annzhang-db/mlflow
May 2023 - Aug 2025
Open source platform for the machine learning lifecycle
Contributions:5 reviews, 4 PRs, 583 pushes in 2 years 4 months
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