Given the recent increase in the prevalence of online food ordering apps, food delivery has become an emerging service. Intelligent dispatch systems have been widely deployed by many on-demand logistics companies to maximize delivery efficiency. Predicting the food delivery time is a key module that provides critical information at the decision-making stage of order dispatch to ensure the punctual delivery service for each customer. In this study, we propose a multitask attention network for food delivery time prediction, mimicking the driver's decision-making process during delivery. First, an attention mechanism is employed to capture mutual influences among orders and evaluate the importance of each order. Then, a multitask learning method is used to simultaneously train delivery time prediction and delivery priority prediction. Finally, a specific loss function is designed to further improve the accuracy of prediction. Extensive modeling demonstrates that our model greatly outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]