Rushali Grandhe
Machine Learning Engineer at Roku
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolRushali Grandhe is a Machine Learning Engineer with eight years of experience building production-ready ML and computer vision systems across startups and enterprise teams. She holds a high-achieving MS from Mohamed bin Zayed University of AI and a BTech from NIT Warangal, and has applied her skills to recommendation engines, NLP/RAG chatbots, image segmentation, and real-time video action detection. Rushali contributed to the ivy project—enhancing framework-agnostic elementwise ops and multi-head attention—to help transpile ML code between NumPy, TensorFlow, and PyTorch, reflecting a focus on interoperability. Her background spans cloud-scale backend engineering at Microsoft to ML productization at Integrated Data Intelligence and Roku, blending research-grade model work with robust engineering. Known for translating cutting-edge models into practical pipelines, she brings both academic rigor (3.95/4) and hands-on experience deploying systems in production.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Technology, 8.94/10, Bachelor of Technology, 8.94/10 at National Institute of Technology WarangalMaster of Science, 3.95/4, Master of Science, 3.95/4 at Mohamed bin Zayed University of Artificial Intelligence
English, Hindi, Telugu, French, Arabic
Github Skills (11)
ivy10
machine-learning10
python10
converter10
numpy10
operator10
tensorflow9
deeplearning-ai9
deep-learning9
pytorch9
jax8
Programming languages (2)
Github contributions (5)
ivy-llc/ivy
Apr 2022 - Sep 2022
Convert Machine Learning Code Between Frameworks
Role in this project:
Backend Developer
Backend DeveloperContributions:92 reviews, 31 commits, 81 PRs in 5 months
Contributions summary:Rushali contributed to the core functionality of the "ivy" project, a machine learning code converter. Their work focused on implementing and modifying elementwise operations, ensuring compatibility across various machine learning frameworks like NumPy, TensorFlow, and PyTorch. These changes included adding new operations, fixing bugs, and adapting existing code to adhere to the Array API Standard, thereby enhancing the project's ability to translate and transpile machine learning code between different platforms. They also made changes to the multi_head_attention function.
pythontensorflowframework-learningtemplatedata-science
rush2406/ivy
Mar 2022 - Jul 2022
The Unified Machine Learning Framework
Contributions:88 pushes, 9 branches in 3 months
fairness-mlpythondata-sciencedeep-learningmachine-learning
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