Eeshan Zele is a software engineer based in San Francisco with eight years of experience focused on safety and reliability of AI systems. He holds a PhD and completed postdoctoral work at MDU in Västerås, bringing deep research rigor to practical ML infrastructure and verification problems. His recent roles span ML infra at Hive and research positions at UIUC where he developed GPU-parallelized SMT solvers, trained and SMT-verified neural networks for indistinguishable-set computation, and uncovered critical blindspots in autonomous racing controllers. He also contributed formal verification work on Path ORAM using Coq, and built performance-evaluation tooling during an Intel internship—demonstrating fluency from low-level systems to high-dimensional verification. Known for pairing formal methods with scalable engineering, he excels at turning theoretical guarantees into production-ready tooling.
8 years of coding experience
Bachelor's degree, Mathematics and Computer Science, 4.0, Bachelor's degree, Mathematics and Computer Science, 4.0 at University of Illinois Urbana-Champaign
Contributions:21 commits, 18 pushes, 1 branch in 25 days
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