Satya Gundabathula
Senior Machine Learning Engineer at Google
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolSatya Gundabathula is a Senior Machine Learning Engineer based in the San Francisco Bay Area with eight years of experience translating state-of-the-art ML into production-ready systems. At Google he has built ground-up AI/ML features across enterprise platforms, improved multimodal representations for YouTube Ads, and advanced Workspace Intelligence and GenAI foundations. His background spans NLP for content safety and document understanding to systems-level performance work from earlier roles at SanDisk, giving him a rare combination of research-caliber modeling and production systems engineering. An active contributor to open-source AutoKeras, he improved its image processing pipeline to better handle diverse datasets—demonstrating attention to practical robustness as well as model quality. He holds a master’s in computer science from Texas A&M and consistently focuses on solutions that bridge prototype research and scalable deployment.
8 years of coding experience
9 years of employment as a software developer
Bachelor's degree, Electronics and Communications Engineering, Bachelor's degree, Electronics and Communications Engineering at National Institute of Technology WarangalMaster's degree, Computer Science, Master's degree, Computer Science at Texas A&M University
Github Skills (9)
keras10
machine-learning10
deep-learning10
tensorflow10
python10
image-processing10
numpy9
scikit8
scikit-learn8
Programming languages (5)
Github contributions (5)
keras-team/autokeras
Oct 2018 - Apr 2019
AutoML library for deep learning
Role in this project:
ML Engineer
ML EngineerContributions:15 commits, 22 PRs, 69 pushes in 5 months
Contributions summary:Satya contributed significantly to the image processing capabilities of the AutoKeras library by implementing image resizing functionalities. They refactored the image processing pipeline by implementing `compute_image_resize_params` and `resize_image_data` functions to handle images of varying sizes and optimize performance. Furthermore, the user modified the `ImageSupervised` class to utilize the new image resizing methods, ensuring consistent image dimensions during model training and evaluation. These changes enhanced the library's ability to handle diverse image datasets, improving its usability.
pytorchpythondeep-learningautomated-machine-learningneural-architecture-search
satyakesav/satyakesav.github.io
May 2024 - Jul 2024
Contributions:18 pushes, 1 branch in 1 month
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