Mudit Jain
Co-Founder And CTO at Stealth Startup
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMudit Jain is a Mountain View–based founder and engineering leader currently serving as Co-Founder and CTO of a stealth startup and a Menlo VC Fellow. He has led major ML and product engineering efforts across Google DeepMind—where he was technical lead for the Gemini agentic platform—and Google Cloud AutoML NLP, and earlier advanced large-scale speech and training pipelines at Microsoft. At VinBrain he grew engineering from a few engineers to five teams and shipped DrAid, an ACM SIGAI Industry Award–winning medical AI product. An active angel investor with 17 early-stage bets and multiple exits, he blends hands-on systems engineering, product strategy, and startup-building. Less obvious: he contributed fixes to Microsoft’s open-source CNTK toolkit that resolved distributed training synchronization and metric-aggregation bugs, improving accuracy and efficiency for large-scale deep learning.
9 years of coding experience
15 years of employment as a software developer
Indian Institute of Technology KanpurComputer Science, Computer Science at Stanford University
French
Stackoverflow
1reputation
25reached
0answers
1question
Github Skills (18)
c-language10
machine-learning10
cntk10
deep-learning10
neural-network10
deep-learning-ai10
c-programming-language10
distributed-systems9
c-plus9
distributed-system9
c-plus-plus9
deep-neural-networks9
python8
parallel-processing6
joblib6
Programming languages (2)
Github contributions (5)
microsoft/CNTK
Feb 2017 - Jun 2018
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:59 commits, 1 PR, 37 pushes in 1 year 4 months
Contributions summary:Mudit focused on improving the BlockMomentumDistributedLearner within the Microsoft Cognitive Toolkit (CNTK), a deep-learning toolkit. Their work involved fixing BMUF metrics averaging and other synchronization issues. The user also contributed to fixing bugs in the metric aggregation process, which prevented duplicate data from being read by workers in the model. These changes aimed to improve the accuracy and efficiency of distributed training in the context of deep learning models.
pytorchpythondeep-learningc-plus-plusmachine-learning
muditjai/Speech
Dec 2016 - Aug 2018
Contributions:25 pushes, 17 branches in 1 year 8 months
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