Directed energy deposition (DED) is a promising metal additive manufacturingtechnology capable of 3D printing metal parts with complex geometries at lowercost compared to traditional manufacturing. The technology is most effectivewhen process parameters like laser scan speed and power are optimized for aparticular geometry and alloy. To accelerate optimization, we apply a data-driven,parameterized, non-linear reduced-order model (ROM) called Gaussian ProcessLatent Space Dynamics Identification (GPLaSDI) to physics-based DED simulationdata. With an appropriate choice of hyperparameters, GPLaSDI is an effectiveROM for this application, with a worst-case error of about 8% and a speed-up ofabout 1,000,000x with respect to the corresponding physics-based data.