Rithwik Ediga Lakhamsani
Software Engineer at Databricks
San Francisco Bay Area United States
Summary
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Rockstar🎓
Top SchoolRithwik Ediga Lakhamsani is a software engineer based in the San Francisco Bay Area with three years of experience building ML systems and distributed training infrastructure. Currently at Databricks, he focuses on integrating PyTorch into large-scale data processing workflows—contributing to Apache Spark to enable distributed PyTorch training, better logging, and improved developer APIs. His background spans internships at Pinterest, Two Sigma, Cloudera, and Amazon, plus research at Berkeley where he applied graph attention networks and RL to image understanding. A UC Berkeley EECS magna cum laude, Rithwik combines production-grade platform engineering with hands-on ML research and a track record of shipping features that bridge model development and scalable deployment. He also has early blockchain and teaching experience, reflecting a breadth of practical and collaborative problem-solving beyond core ML work.
3 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Electrical Engineering and Computer Science, 3.98 GPA (Magna Cum Laude), Bachelor's degree, Electrical Engineering and Computer Science, 3.98 GPA (Magna Cum Laude) at UC Berkeley College of EngineeringHigh School Diploma, 4.37 GPA, High School Diploma, 4.37 GPA at Folsom High School
English, French, Telugu
Github Skills (10)
pytorch10
big-data10
machine-learning10
spark10
distributed-training10
python10
apidoc9
mlops9
api9
scala8
Programming languages (4)
Github contributions (5)
apache/spark
Jan 2023 - Jan 2023
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
ML Engineer
ML EngineerContributions:92 reviews, 7 commits, 16 PRs in 15 days
Contributions summary:Rithwik primarily contributes to the development of PyTorch integration within the Apache Spark ecosystem. Their work includes designing and implementing APIs for distributed PyTorch training using files and functions, enhancing local and distributed training capabilities, and adding necessary logging features for debugging and monitoring. The user's contributions focused on building a framework that allows users to leverage PyTorch models for large-scale data processing within the Spark environment. The user also addressed error handling and improved documentation to enhance user experience.
analyticspythondata-processingsqlapache
rithwik-db/spark
Dec 2022 - Mar 2023
Apache Spark - A unified analytics engine for large-scale data processing
Contributions:75 pushes, 14 branches in 2 months
analyticsdata-processingsqlapachebig-data
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