Machine Learning Engineer with Mathematical Foundations

My background is unusual for a machine learning engineer: I spent years doing pure mathematics research — topological dynamics, ergodic theory, measure theory — before deliberately building toward applied ML. That foundation means I don't just apply algorithms; I understand why they work, where they break, and how to design systems that are mathematically sound. I'm currently seeking ML engineering or applied research roles where that rigour is an asset.