Adi Ganesh
Member Of Technical Staff at OpenAI
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolAdi Ganesh is a Member of Technical Staff at OpenAI with 13 years of machine learning and software engineering experience, focused on post-training research for large neural models. Previously he trained multi-billion-parameter transformers for recommendation at Meta and led perception model work for self-driving at Nuro, spanning 3D detection, segmentation, and OOD detection. He brings hands-on experience across industry and academia, having taught functional programming at Stanford and contributed to core open-source projects like scikit-learn to improve install reliability and dependency checks. Early work includes ML for genomics and Autopilot perception at Tesla, and co-founding a healthcare startup as a Thiel Fellow, reflecting an appetite for applied research and productization. Based in Palo Alto, he combines deep theoretical training (MS CS, BS Math from Stanford) with practical system-level rigor and a knack for improving developer experience behind the scenes. Colleagues describe him as a researcher-engineer who moves fluidly between model scaling experiments and production-ready tooling.
13 years of coding experience
7 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Stanford University
Github Skills (8)
machine-learning10
scipy10
python10
data-science10
numpy10
setuptools9
py9
data-analysis8
Programming languages (8)
Github contributions (5)
scikit-learn/scikit-learn
Sep 2015 - Sep 2015
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:5 commits, 3 PRs, 7 comments in 8 days
Contributions summary:Adi's contributions focused on improving the installation and dependency management of the scikit-learn library. They implemented checks to ensure NumPy and SciPy met minimum version requirements, and amended error messages to provide more informative feedback to users. Furthermore, the user added features to output out-of-date SciPy/NumPy versions, improving the user experience during setup. Finally, the user also made the setup.py file PEP8 compliant.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
acganesh/torcspilot
Nov 2016 - Aug 2019
Contributions:29 commits, 2 pushes in 2 years 9 months
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