An exciting application area of machine learning and deep learning methods is completion, repair, synthesis, and automatic explanation of program code. This field has received a fair amount of attention in the last decade (see e.g. Oda et al. (2015); Balog et al. (2017); Allamanis et al. (2018)), yet arguably the recent application of large scale language modelling techniques to the domain of code holds a tremendous promise to completely revolutionize this area (Chen et al., 2021; Austin et al., 2021). The new large pretrained models excel at completing code and synthesizing code from natural language descriptions; they work across a wide range of domains, tasks, and programming languages. The excitement about new possibilities is spurring tremendous interest in both industry and academia. Yet, we are just beginning to explore the potential of large-scale deep learning for code, and state-of-the-art models still struggle with correctness and generalization. This calls for platforms to exchange ideas and discuss the challenges in this line of work. The second Deep Learning for Code (DL4C) workshop will provide such a platform at ICLR 2023.