About

I’m Logan Rudd, an ML infrastructure engineer for physical systems.

My background is physics instrumentation R&D, working on the data acquisition and control systems for spaceflight hardware (NASA JPL), muon detectors (Fermilab), and dark-matter optics (Berkeley Lab) — the half of my profile where the data comes from instruments and the failure modes are physical. That work is documented in peer-reviewed instrumentation publications, including Measurements of angle-resolved reflectivity of PTFE in liquid xenon with IBEX (EPJ C, 2020).

I then moved into production machine learning, building and operating ML at fintech scale — previously at Nubank and Kueski. That’s where I learned to treat infrastructure as the product: the parts of an ML system that decide whether it survives contact with real traffic, real data, and real cost.

Today I work at the intersection of the two — the ML platform and infrastructure work that physical-systems teams (aerospace, defense, energy, robotics) need, and that general ML-platform teams need regardless of domain.

Elsewhere: GitHub · LinkedIn · loganrudd@gmail.com