Jake Schmidt is a research engineer with 9 years of experience building large-scale ML systems for biology and industry, currently leading model and infrastructure design for spatial multiomics foundation models. He founded the ML research function at NOETIK, drove a 25% accuracy gain through architecture and evaluation improvements, and engineered distributed training optimizations that enable 10–20x throughput and 100x longer context windows. Previously at Recursion he overhauled petabyte-scale inference and experiment lifecycle systems for drug discovery, and he has hands-on experience translating legacy scientific pipelines to reproducible Python implementations. Jake blends deep domain expertise in computer vision for drug discovery with rigorous engineering practices—experiment tracking, model registries, and fault-tolerant training—that bridge research and production. His background in physics and business analytics, plus a history of teaching ML workflows to researchers, gives him a rare mix of quantitative rigor, product-aware engineering, and science communication. An intriguing through-line: he pairs large-scale distributed systems work with biological insight generation, enabling both faster training and more interpretable models for experimentalists.
9 years of coding experience
6 years of employment as a software developer
Bachelor’s Degree, Physics, Honors, 3.7, Bachelor’s Degree, Physics, Honors, 3.7 at The University of Texas at Austin
Master of Science - MS, Business Analytics, Master of Science - MS, Business Analytics at The University of Texas at Austin - Red McCombs School of Business
Contributions:21 pushes, 2 branches, 1 comment in 1 year 3 months
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