Senior AI/ML Engineer

Job description

Description About the Team The Compression and Parity team in GM's Autonomous Vehicle organization makes aggressive model optimization safe enough to ship repeatedly. We compress and quantize models headed for the car, and we own the analytical machinery that proves the compressed model still behaves like the original - not by assertion, but with bounded, quantified, auditable evidence. Every deployment decision our organization makes about a compressed model runs through the tooling this team builds. About the Role We are looking for a mathematically rigorous engineer to own model numerics: how we measure, bound, and reason about the numerical behavior of the models we ship - and how we turn that analysis into deployment decisions. The central question of this role is deceptively simple: given two numerically different versions of the same model, is the difference safe? Answering it well requires connecting things that are usually studied separately - floating-point drift and Hessian conditioning on one end, vehicle trajectory error on the other. You will build the tooling that makes that connection quantitative, and you will define the thresholds that turn it into a ship / no-ship decision. This role is not: running an existing validation harness and reporting the numbers it produces. When a parity check fails, the expectation is that you can say which operation caused the divergence and why - not merely that a difference exceeded a threshold. The tooling exists to make that investigation fast; it does not replace the investigation itself. What You'll Do Validate Optimized implementations . Optimized implementations are supposed to be equivalent to their references. Establishing that rigorously, rather than by spot check, means deciding what equivalence should mean for a given operation, and designing the inputs that would expose a violation if one existed. Connect tensor differences to behavioral disparity. Map low-level numerical differences from quantization, compilation, and precision reduction to downstream driving behavior, using both open-loop metrics (trajectory displacement error, perception IoU) and closed-loop outcomes - and identify the mechanism behind the mapping, not just the correlation. Build sensitivity and robustness analysis tooling . Use Jacobian/Hessian-based methods to characterize how model outputs respond to weight and input pertu…