Pinterest consolidated its ad engagement models from three separate, surface-specific models into a single unified architecture. This change aimed to address inefficiencies like slow iteration, redundant training costs, and maintenance burdens. The project followed a strategy prioritizing simplicity and safe iteration, starting with merging the strongest components. The initial baseline unified model saw offline improvements but increased costs, leading to further refinement. The refined architecture incorporated elements from different surfaces, like MMoE and long user sequences, achieving better results with a more reasonable cost. Surface-specific calibration was implemented to handle traffic distribution differences across surfaces effectively. Multi-task learning and surface-specific exports were introduced for flexibility and surface-specific iterations. Efficiency optimizations, including projection layers and request-level broadcasting, reduced infrastructure costs and latency. The unified model demonstrated significant improvements in both offline and online metrics. This consolidation enables faster and more consistent improvements. Finally, the next step involves unifying the Related Pins surface, with a focus on model efficiency.