Shubham Gupta
Machine Learning Engineer at Google DeepMind
Sunnyvale, California, United States
Summary
👤
Senior🎓
Top SchoolShubham Gupta is a machine learning engineer and tech lead with over a decade of experience building production-grade NLP and agentic AI systems, currently contributing to Microsoft AI after a recent Staff ML Engineer role at Google DeepMind on Gemini Deep Research. He has led ML platform and MLOps initiatives at Cresta and DocuSign that scaled real-time intelligence for contact centers and accelerated model development pipelines. His background includes impactful ML and conversational AI work at Meta and substantive research training from a Master's at Stanford, where he also contributed to the well-regarded SNAP graph analysis library by refining centrality measures. Known for bridging research and product, he combines low-level algorithmic improvements with platform thinking to ship features like self-serve intent creation and out-of-box LLM models. Based in Sunnyvale, he blends academic rigor with startup velocity and a track record of improving model quality and developer productivity across large-scale systems.
11 years of coding experience
10 years of employment as a software developer
Indian Institute of Technology KanpurMaster's Degree, Computer Science, Master's Degree, Computer Science at Stanford University
English, Hindi
Github Skills (7)
algorithm10
data-structures10
algorithms10
c-language10
cprogramming-language10
network-analysis10
data-structure10
Programming languages (1)
Github contributions (5)
snap-stanford/snap
Oct 2015 - Nov 2015
Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 commits, 1 comment in 14 days
Contributions summary:Shubham made several commits related to the implementation and modification of centrality measures within the SNAP library. They removed undirected versions of centrality calculations and enabled normalized closeness centrality. The user also added an isDirected flag to various centrality functions and fixed issues related to betweenness centrality calculations. These changes suggest a focus on refining and extending existing network analysis functionalities.
graph
HazyResearch/CaffeConTroll
Jan 2015 - Feb 2015
Contributions:116 commits in 27 days
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