Michael Pilosov is a Director at Slalom with a decade of experience translating applied mathematics and PhD-level research into production-grade AI/ML systems and team practices. He specializes in scaling machine learning, defining maintainable architectures, and fostering iterative, collaborative workflows that enable continuous, confident shipping. His doctoral work in inverse-problem measure-theoretic methods informs a rare blend of rigorous statistical thinking and practical engineering for robustness under uncertainty. Based in Denver, he previously advanced through solution architect and principal roles at Slalom and has a track record of teaching and research at the University of Colorado Denver and Los Alamos. He is committed to democratising reproducible, accessible data-driven decision making by packaging not just open code but usable computational environments and documentation. Colleagues know him for balancing deep quantitative insight with pragmatic delivery and team productivity improvements.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Applied Mathematics, Doctor of Philosophy (Ph.D.) Applied Mathematics at University of Colorado Denver
Bachelor's Degree Mathematics, Bachelor's Degree Mathematics at SUNY Geneseo
Contributions:6 PRs, 77 pushes, 3 branches in 4 months
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