Recently, deep convolutional neural networks (CNNs) have started to be applied for automatic target recognition (ATR) problems of synthetic aperture radar (SAR) images. In conventional SAR-ATR algorithms, the pose angle information of the target has been importantly used. However, recent deep learning-based SAR-ATR algorithms often only utilize the intensity information. In this paper, based on the prior works that the pose angle is an important latent variable for boosting target recognition performance, we propose a CNN-based SAR target recognition network with pose angle marginalization learning, called SPAM-Net that marginalizes the conditional probabilities of SAR targets over their pose angles to precisely estimate the true class probabilities. The proposed SPAM-Net consists of two sub-nets: (i) a sub-net for class-conditional probability estimation, called CP sub-net, and (ii) a sub-net for pose angle probability estimation, called PP sub-net. The two sub-nets are jointly learned via an end-to-end manner in a Bayesian framework so that the SPAM-Net incorporates the pose angle information into target recognition task effectively. The SPAM-Net outperforms our baseline network that does not utilize the pose angle information. In the experiments, we intensively analyze the effectiveness of pose angle information for SAR-ATR, revealing that more accurate pose angle information helps the SPAM-Net precisely estimate target classes for the misclassified target groups that are obtained by the baseline network. Furthermore, our method also outperforms the other state-of-the-art SAR-ATR algorithms, yielding the correct target recognition rate with average 99.61%.