Swapnal Varma
Senior Software Engineer at Samsung R&D Institute India - Bangalore
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolSwapnal Varma is a Senior Software Engineer at Samsung Research Bangalore with five years of hands-on experience building large-scale data systems, AI infrastructure and production ML for foundation vision models. He blends ML research (hyperbolic manifold learning, Fourier methods, dataset condensation, latent diffusion) with systems engineering expertise in high-volume event streams, distributed compute, kernel/driver work and deep learning compiler design. Notable for rapid, high-impact delivery, he earned multiple Samsung excellence awards—including the rare Super Tech award as an intern—and presented research at iAIM-2023. Swapnal is an active open-source contributor (TFX examples) and has demonstrated the ability to squeeze dramatic performance gains (up to 400x in prototypes) while reducing compute and memory footprints via profiling, vectorization and algorithmic redesign. He’s equally comfortable taking features from ideation to MD/CTO-level presentations and collaborating across research and production teams.
5 years of coding experience
2 years of employment as a software developer
Bachelor of Technology - BTech, Computer Science, Bachelor of Technology - BTech, Computer Science at Vellore Institute of Technology
Github Skills (8)
pandas10
jupyter-notebook10
machine-learning10
tensorflow10
python10
data-preprocessing9
nlp8
documentation7
Programming languages (8)
Github contributions (5)
tensorflow/tfx
Sep 2021 - Sep 2021
TFX is an end-to-end platform for deploying production ML pipelines
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 12 commits, 1 PR in 4 days
Contributions summary:Swapnal contributed to the development and maintenance of machine learning examples within the TFX framework. They added and modified a Jupyter Notebook for fetching and preprocessing the IMDB dataset, demonstrating an understanding of data loading and preparation for sentiment analysis. The user also addressed markdown rendering issues and included dataset source acknowledgements, further showcasing their involvement in the example's usability and documentation. Additionally, they incorporated modifications from the main branch, indicating collaboration and integration within the project.
deployingend-to-endml-pipelinesmlmlops
swap-10/ImageApp
Nov 2021 - Sep 2022
An full stack web app facilitating storage and granular sharing of images
Contributions:17 PRs, 40 pushes, 2 branches in 10 months
reactfull-stacksharinggranularnodejs
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