Natural language processing (NLP) tasks (e.g. question-answering in English) benefit from knowledge of other tasks (e.g., named entity recognition in English) and knowledge of other languages (e.g., question-answering in Spanish). Such shared representations are typically learned in isolation, either across tasks or across languages. In this work, we propose a meta-learning approach to learn the interactions between both tasks and languages. We also investigate the role of different sampling strategies used during meta-learning. We present experiments on five different tasks and six different languages from the XTREME multilingual benchmark dataset (Hu et al., 2020). Our meta-learned model clearly improves in performance compared to competitive baseline models that also include multitask baselines. We also present zero-shot evaluations on unseen target languages to demonstrate the utility of our proposed model.