Samaneh Saadat
ML Software Engineer at Google
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolSamaneh Saadat is an ML Software Engineer with a decade of experience building production-ready machine learning systems, currently working on AutoML Translation at Google in Seattle. She holds a PhD in Computer Science and has a strong academic foundation in algorithms and computation, which she applies to real-world ML challenges. Her open-source contributions to the widely used Keras project — including fixes for multi-process JAX backend distribution and new model variable APIs — highlight practical expertise in model deployment and tooling. Prior roles span robotics research and graduate research at UCF, reflecting a blend of applied engineering and rigorous research. Colleagues rely on her ability to translate complex research into robust, scalable components that improve model flexibility and multi-process reliability.
9 years of coding experience
9 years of employment as a software developer
Bachelor's Degree, Software Engineering, Bachelor's Degree, Software Engineering at Iran University of Science and TechnologyMaster's Degree, Algorithms and Computations, Master's Degree, Algorithms and Computations at University of TehranDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of Central Florida
English, Persian
Stackoverflow
61reputation
2kreached
0answers
1question
Github Skills (11)
neural-network10
machine-learning10
deep-learning10
jax10
tensorflow10
python10
data-science9
locking6
boost6
mutex6
pytorch4
Programming languages (3)
Github contributions (5)
keras-team/keras
Aug 2016 - Mar 2025
Deep Learning for humans
Role in this project:
ML Engineer
ML EngineerContributions:29 reviews, 8 PRs, 57 comments in 8 years 8 months
Contributions summary:Samaneh primarily contributed to the Keras library, demonstrating expertise in machine learning model development and distribution strategies. They focused on fixing data and model distribution issues, particularly related to multi-process scenarios within the JAX backend. The user also implemented new APIs for accessing and manipulating model variables, significantly enhancing the library's flexibility.
pythondata-sciencedeep-learningneural-networksmachine-learning
SamanehSaadat/ExplainingDifferencesInDiscreteSequences
May 2019 - Jun 2020
Contributions:7 commits, 4 pushes, 1 branch in 1 year
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