Amy Skerry-ryan
Senior Staff Software Engineer at Google DeepMind
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAmy Skerry-ryan is a Senior Staff Software Engineer with 11 years of experience building production ML tooling and large-scale data systems, currently accelerating AI adoption at Google DeepMind after leading ML lifecycle tooling across Google. She combines a strong research background—a PhD-focused trajectory in cognitive neuroscience from Harvard/MIT—with hands-on engineering, having shipped personalized recommendation and data pipelines at Khan Academy and contributed to core open-source projects like TensorFlow Estimator. Her open-source work includes enhancing Boosted Trees support and FeatureColumn V2 in tensorflow/estimator, and she has a track record of improving test reliability and maintainability in projects such as Jupyter and KA Lite. Comfortable spanning backend, ML model engineering, and test automation, she brings a pragmatic rigor to both prototyping and productionization. A former volunteer leader for Tech for Campaigns, she also applies data-driven decision making to high-impact, real-world problems beyond industry.
11 years of coding experience
10 years of employment as a software developer
Grad Student Affiliate Brain and Cognitive Sciences, Grad Student Affiliate Brain and Cognitive Sciences at Massachusetts Institute of TechnologyDoctor of Philosophy (Ph.D.) Psychology, Doctor of Philosophy (Ph.D.) Psychology at Harvard UniversityBachelor's degree Cognitive Science, Bachelor's degree Cognitive Science at Yale UniversityMedfield High School
Github Skills (20)
pytest10
selenium10
python10
django10
testing10
machine-learning10
selenium-ide10
api10
tensorflow10
test-automation10
trainings9
deep-learning9
modeling9
jupyter-notebook8
html8
Programming languages (6)
Github contributions (5)
tensorflow/estimator
Aug 2019 - Aug 2021
TensorFlow Estimator
Role in this project:
ML Engineer
ML EngineerContributions:10 commits in 1 year 11 months
Contributions summary:Amy primarily contributed to the `tensorflow/estimator` repository by modifying code related to the Boosted Trees model within the TensorFlow framework. Their work included enhancing the model's capabilities by allowing arbitrary DenseColumns and supporting FeatureColumn V2, which are core components of the estimator API. They also fixed tests related to the Boosted Trees model, and addressed training mode propagation. The user also allowed integer NumericColumns to be bucketized in GBDT.
deep-learningmachine-learningtensorflowtensorflow-estimatorestimator
jupyter/notebook
Oct 2018 - Nov 2018
Jupyter Interactive Notebook
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:10 commits, 3 PRs, 7 comments in 19 days
Contributions summary:Amy primarily focused on migrating existing JavaScript-based tests to Selenium for the Jupyter notebook project. They refactored tests for image display, merge cells, and display isolation, adapting them to use the Selenium framework for automated browser testing. Additionally, they added functionality to support waiting for multiple elements and refined the existing Selenium test utility functions to avoid race conditions.
jupyter-notebooknotebookjupyterlabnotebooksjupyter
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