Gogul Balakrishnan
Technical Lead at Google DeepMind
California, United States
Summary
🤩
Rockstar🎓
Top SchoolGogul Balakrishnan is a Technical Lead with 8+ years building ML-infused compilers, program analysis, and large-scale developer tools at Google and DeepMind. He has led teams delivering GenAI code-generation products, privacy-preserving analysis for Android’s Private Compute Core, and vulnerability-detection tooling for Google Play, bridging research and production. His background includes research contributions to Swift for TensorFlow and contextual code embeddings (CuBERT), and hands-on open-source work on Swift for TensorFlow and fastai experiments focused on data pipelines and performance. Comfortable across static/dynamic analysis, compilers, and ML systems, he combines deep academic training (PhD, UW–Madison) with pragmatic engineering that ships at scale. An understated strength is his ability to move ideas from compiler research into developer-facing GenAI tools that measurably improve programmer productivity.
8 years of coding experience
11 years of employment as a software developer
Ph.D Computer Science, Ph.D Computer Science at University of Wisconsin-MadisonB.E Computer Science, B.E Computer Science at College of Engineering, Guindy
Github Skills (11)
computer-vision10
swift10
deep-learning10
tensorflow10
data-processing9
differentiable-programming9
machine-learning8
unit-testing8
dockers7
test-automation7
docker7
Programming languages (9)
Github contributions (5)
tensorflow/swift-apis
Jun 2019 - Sep 2019
Swift for TensorFlow Deep Learning Library
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:60 commits, 125 PRs, 66 pushes in 3 months
Contributions summary:Gogul primarily contributed to the Swift for TensorFlow deep learning library by updating the codebase, fixing bugs, and refactoring code. They modified Dockerfiles to utilize the test tool chain and addressed failing tests. Furthermore, the user reorganized test files within the repository and added new requirements to the TensorGroup and TensorArrayProtocol. They also focused on performance by making adjustments to seed types and fixing style issues.
differentiable-programmingswift-for-tensorflowdeep-learningmachine-learningdeep-learning-library
fastai/fastai_dev
Apr 2019 - Aug 2019
fast.ai early development experiments
Role in this project:ML Engineer
ML EngineerContributions:7 commits, 9 PRs, 7 pushes in 4 months
Contributions summary:Gogul contributed to the early development of a machine learning project, specifically focused on image processing and dataset manipulation within a Swift environment. They implemented image resizing functionalities using the Dataset API and made updates to data batching operations. The user also added helper functions for preparing data batches from image files, suggesting a focus on the data pipeline and model training preparation aspects.
pythondata-sciencemachine-learningfastainbdev
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