Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline integrated into the ReaderBench platform designed to simplify the process of training AES models by automating both feature extraction and architecture tuning for any multilingual dataset uploaded by the user. The dataset must contain a list of texts, each with potentially multiple annotations, either scores or labels. The platform includes traditional ML models relying on linguistic features and a hybrid approach combining Transformer-based architectures with the previous features. Our method was evaluated on three publicly available datasets in three different languages (English, Portuguese, and French) and compared with the best currently published results on these datasets. Our automated approach achieved comparable results to state-of-the-art models on two datasets, while it obtained the best performance on the third corpus in Portuguese.