Mohit Reddy
Member Of Technical Staff at OpenAI
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMohit Reddy is a seasoned software engineer with 11 years of experience building large-scale distributed systems and production ML infrastructure, currently working on AGI at OpenAI. He has led systems and research-focused engineering at xAI and Google, co-founded and scaled real-time feature engineering at Fennel (acquired by Databricks), and contributed to the Flax JAX library as an early maintainer. Mohit combines deep systems engineering—autoscaling, control plane reliability, and TPU/JAX integration—with applied ML model work (lead contributor on Grok series and computer-use agents). He’s pragmatic about shipping: founding teams, shipping infra used by hundreds of researchers, and improving test and CI rigor in open-source projects. Based in Menlo Park, he pairs startup founder instincts with large-org technical leadership and a track record of mentoring engineers across research and production teams. Notably, he’s an active OSS contributor who improved Flax’s test infrastructure and added graph neural network tests, reflecting a focus on quality and reproducibility.
11 years of coding experience
8 years of employment as a software developer
Kendriya Vidyalaya SangathanBachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at National Institute of Technology Karnataka
English, Hindi, Telugu
Github Skills (7)
machine-learning10
jax10
pytest10
python10
test-automation10
graph-neural-network9
tensorflow8
Programming languages (14)
Github contributions (5)
google/flax
Apr 2020 - Oct 2020
Flax is a neural network library for JAX that is designed for flexibility.
Role in this project:
ML Engineer & Test Automation Engineer
ML Engineer & Test Automation EngineerContributions:40 reviews, 72 commits, 39 PRs in 5 months
Contributions summary:Mohit contributed to the Flax library by addressing typos, enhancing test infrastructure, and adding new test cases. Their work includes fixing documentation errors, integrating pytest for improved warning management and test execution, and refactoring existing tests. The user also introduced a new test for graph neural networks, demonstrating their focus on improving test coverage and the quality of the library's examples.
deep-learningneural-networksneural-networkflaxjax
mohitreddy1996/Distributed-File-System
Mar 2017 - Apr 2017
Contributions:26 commits, 20 pushes, 1 branch in 13 days
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