Ravin Kumar
Senior Researcher at Google DeepMind
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRavin Kumar is a Senior Researcher based in Los Angeles with 11 years of experience building production-grade generative and Bayesian models and shipping ML systems at scale. Currently at Google DeepMind after leading GenAI safety and product efforts at Google, he combines research rigor with hands-on engineering to drive trustworthy LLM deployments. His open-source contributions to PyMC, ArviZ, Bambi and JAX documentation reflect deep expertise in probabilistic programming and making Bayesian workflows more usable and pedagogical. Earlier roles at SpaceX and sweetgreen show a track record of translating analytics into operational impact—from launch planning software to near-real-time restaurant operations. Known for working on the Gemini/Gemma series, he brings both academic depth and pragmatic product focus to safety-critical generative AI.
11 years of coding experience
11 years of employment as a software developer
Master of Science (MSc) Manufacturing Systems Engineering, Master of Science (MSc) Manufacturing Systems Engineering at University of Wisconsin-MadisonCal Poly Pomona
Stackoverflow
1reputation
0reached
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Github Skills (34)
code-readability10
data-visualizations10
bayesian-statistics10
probabilistic-programming10
restructuredtext10
bayesian-data-analysis10
python10
py10
data-science10
testing10
mathematics10
statistics10
data-visualisation10
math10
statistic10
Programming languages (18)
Github contributions (5)
pymc-devs/pymc-resources
Jul 2018 - May 2022
PyMC educational resources
Role in this project:
Data Scientist
Data ScientistContributions:18 reviews, 33 commits, 63 PRs in 3 years 9 months
Contributions summary:Ravin contributed to the PyMC-resources repository by fixing plot labels and addressing typos within the Rethinking notebooks. These changes indicate a focus on improving the clarity and accuracy of the educational resources. The edits suggest this user has a background in data analysis and Bayesian inference.
data-analysispythondata-sciencepymcbayesian-inference
pymc-devs/pymc4
Jan 2019 - Nov 2019
Experimental PyMC interface for TensorFlow Probability. Official work on this project has been discontinued.
Role in this project:Back-end Developer
Back-end DeveloperContributions:67 commits, 36 PRs, 25 pushes in 10 months
Contributions summary:Ravin contributed to the PyMC4 project, which focuses on a probabilistic programming interface for TensorFlow Probability. Their work involved modifications to core model components, specifically adjusting the evaluation and return methods to enhance code clarity. The user also implemented variable naming for easier graph inspection, as well as included setup files to facilitate package portability. Finally, they addressed code compilation issues.
pythonpymcmachine-learningdiscontinuedprobability
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