Summary
Victor Minden is a Staff Research Engineer with a PhD in Computational and Mathematical Engineering and 16 years of experience bridging research and production-grade ML systems. He specializes in translating large Transformer-based models between frameworks and optimizing them for custom accelerators, most recently compiling models to run on the Cerebras Wafer-Scale Engine and working on reinforcement learning at DeepMind. His background spans numerical optimization, parallel GPU implementations, and production data pipelines—from optimal power flow and stochastic optimization at X to pathology image algorithms and compiler-driven kernel generation. Comfortable toggling between theory and systems, he has led small teams, driven software engineering best practices in research settings, and published novel numerical methods during his postdoc and national-lab work. A computational mathematician by training, he brings deep expertise in algorithm design that informs pragmatic, high-performance ML engineering.
15 years of coding experience
6 years of employment as a software developer
Bachelor of Science (BS) Electrical Engineering Mathematics, Bachelor of Science (BS) Electrical Engineering Mathematics at Tufts University
Doctor of Philosophy (PhD) Computational and Mathematical Engineering (CME), Doctor of Philosophy (PhD) Computational and Mathematical Engineering (CME) at Stanford University
English