Nanubala Sai
MARS Fellow at Shipsy
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolNanubala Sai is a research-oriented software engineer based in Bengaluru with seven years of experience building scalable machine learning and logistics systems at Shipsy and in open-source projects. He has delivered production-grade routing and territory-optimization services that scaled to 246k daily calls and reduced error rates to near-zero, and engineered caching and AWS Batch solutions to dramatically cut latency and costs. His research work bridges AI safety and practical ML: contributions to mlpack, TensorFlow Lite support, and a knowledge-graph project at EleutherAI demonstrate fluency from low-level C++ inference engines to large-scale embedding pipelines. A published researcher and GSoC mentor, he combines a vision for AI solving global challenges with hands-on rigor—stubborn persistence, as he puts it—to turn ambitious ideas into measurable impact.
7 years of coding experience
3 years of employment as a software developer
Bachelor of Technology - BTech Electrical Electronics and Communications Engineering, Bachelor of Technology - BTech Electrical Electronics and Communications Engineering at Indian Institute of Information Technology, SriCity
Github Skills (18)
c-language10
image-processing10
machine-learning10
reinforcement-learning10
deeplearning-ai10
tensorflow-lite10
deep-learning10
computer-vision10
cprogramming-language10
kernel-methods10
data-science9
testing9
linear-algebra9
computer-engineering9
ml-deployment8
Programming languages (7)
Github contributions (5)
tensorflow/tflite-support
Jul 2021 - Sep 2021
TFLite Support is a toolkit that helps users to develop ML and deploy TFLite models onto mobile / ioT devices.
Role in this project:
ML Engineer
ML EngineerContributions:43 reviews, 63 commits, 8 PRs in 1 month
Contributions summary:Nanubala's contributions primarily focused on modifying and testing image processing components within the TFLite Support library. Their work involved adjusting image tensor specifications to handle dynamic input shapes, setting up tests related to dilated convolution and image preprocessing, and validating output dimensions. They implemented and refined code related to input resizing and image data normalization to ensure proper function within the TensorFlow Lite environment, including setting up test cases to check the dimensions of the output tensor after processing.
iot-devicesmachine-learningtflite-modelstensorflowtflite
shogun-toolbox/shogun
Feb 2020 - Jul 2020
Shōgun
Role in this project:Back-end Developer
Back-end DeveloperContributions:5 reviews, 15 commits, 33 PRs in 4 months
Contributions summary:Nanubala made several code changes to the Shogun toolbox, primarily focusing on kernel-related improvements and refactoring. They removed a CStreamingKernel from Kernel.h. They fixed an issue related to SVMLight.cpp and updated the code to accept feature arguments in KernelDensity.cpp and KernelDensity.h. Additionally, the user added mathematical functions like sin and cos to the linalg package.
cmakedata-sciencegunc-plus-plusmachine-learning
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