Deep learning is a rapidly-evolving technology with the possibility to significantly improve the physics reach of collider experiments. In this study we developed a novel vertex finding algorithm for future lepton colliders such as the International Linear Collider. We deploy two networks: one consists of simple fully-connected layers to look for vertex seeds from track pairs, and the other is a customized Recurrent Neural Network with an attention mechanism and an encoder–decoder structure to associate tracks to the vertex seeds. The performance of the vertex finder is compared with the standard ILC vertex reconstruction algorithm.