Sravan J - Software Engineer at Google

Sravan J

Software Engineer at Google

San Jose, California, United States

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Summary

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Rockstar

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Top School

Sravan J is a software engineer based in San Jose with over a decade of professional experience and a strong foundation in Java, C++, Python, Android development, and big data technologies like Hadoop and Hive. He has worked at leading tech companies including Google, Apple, Bloomberg, and NVIDIA, contributing to both backend systems and machine learning tooling. Sravan’s open-source contributions include bug fixes and backend enhancements to NVIDIA's DIGITS deep-learning training system, improving model selection, validation, and error handling. A competitive programmer qualified for multiple Facebook Hacker Cups and Google Code Jam, he brings solid algorithm and data-structure skills to complex engineering problems. His background includes hands-on Hadoop performance work at Intel and practical Android innovation from hackathon projects, reflecting a blend of research-oriented and product-focused experience.

12 years of coding experience

6 years of employment as a software developer

San José State University

B.Tech, Computer Science & Engineering, B.Tech, Computer Science & Engineering at Jawaharlal Nehru Technological University (JNTU)

English, Telugu, Hindi

Stackoverflow

Stats

1reputation

0reached

0answers

0questions

Github Skills (11)

python10

flask10

flask-ask10

deeplearning-ai9

machine-learning9

deep-learning9

caffe8

html7

javascript7

git6

gpu6

Programming languages (3)

LuaHTMLPython

Github contributions (5)

NVIDIA/DIGITS

Mar 2015 - Sep 2015

Deep Learning GPU Training System

Role in this project:

Back-end Developer

Contributions:9 commits, 3 PRs, 27 pushes in 5 months

Contributions summary:Sravan primarily focused on fixing bugs and enhancing the functionality of the deep learning training system. Their contributions involved resolving issues related to model selection, including changes in the user interface (HTML, Javascript) to improve the selection and validation of pre-trained models. Furthermore, the user implemented modifications to the backend (Python, Flask) to fix validation issues, included encode value in dataset & job information, improved the error handling. Their work also included refactoring the code, by moving the default_batch_size to constants file.

deep-learninggputorchmachine-learningtraining

Sravan2j/TileWars

Dec 2013 - Mar 2014

Contributions:32 commits in 2 months

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