Siddharth Gollapudi is a researcher-engineer with 11 years of experience building scalable search and ML systems, currently a Graduate Student Researcher in UC Berkeley EECS and a PhD candidate in Computer Science. He spent several years at Microsoft’s algorithms group pioneering hybrid and multivector extensions to DiskANN for large-scale vector search, translating research ideas into production-ready systems. His background includes internships at AWS (where he rewrote the ROS2 cross-compilation tool and productionized its metrics dashboard) and research work at Stanford applying TensorFlow models to proteomics and building cloud-backed search prototypes. Comfortable at the intersection of algorithms, systems, and applied ML, he combines rigorous academic training with hands-on engineering that ships. Based in Cupertino, he brings a habit of accelerating tooling and reproducibility—evident in dashboarding and performance wins—alongside deep interest in search and vector-based representations.
11 years of coding experience
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of California, Berkeley
N/A, Computer Science, N/A, Computer Science at De Anza College
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