The goal of system identification (SysID) is to learn a mathematical model from a corpus of temporal observations of a system’s inputs and outputs. SysID is a fundamental problem in engineering, with applications ranging from circuit design to robot control. Classical approaches to linear SysID rely on convex relaxations that offer strong theoretical guarantees. However, these methods often suffer from scalability issues and are not well-suited for large-scale systems. In this talk, I will present a nonconvex optimization approach to linear SysID that overcomes these limitations. It is well known that converting convex problems to nonconvex ones can lead to significant computational advantages, but they typically lack theoretical guarantees. I will show how we managed to achieve the best of both worlds: a faster and theoretically sound algorithm.