Machine Learning (ML) is on the rise as a tool for cryospheric sciences. It has been used to label, cluster, and segment cryospheric components, as well as emulate, project, and downscale cryospheric processes. To date, the cryospheric community mainly adapts and develops ML approaches for highly specific domain tasks. However, different cryospheric tasks can face similar challenges, and when an ML method addresses one problem, it might be transferable to others. Thus, we invite the community to share their current work and identify potential shared challenges and tasks. We invite contributions across the cryospheric domain, including snow, permafrost, glaciers, ice sheets, and sea ice. We especially call for submissions that use novel machine learning techniques; however, we welcome all ML approaches, ranging from random forests to deep learning. Other contributions, such as datasets, theoretical research, and community-building efforts, are also welcome. By identifying shared challenges and transferring knowledge, we aim to channel resources and increase the impact of ML as a tool to observe, assess, and model the cryosphere.