Snow-layer segmentation and classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. To this end a Snow Micro Pen (SMP) can be used - a portable high-resolution snow penetrometer. However, the penetration-force measurements of the SMP must be labeled manually, which is a time-intensive task that requires training and becomes infeasible for large datasets. Here, we evaluate how well machine learning models can automatically segment and classify SMP profiles. Fourteen different models are trained on the MOSAiC SMP dataset, a unique and large SMP dataset of snow on Arctic sea-ice profiles. Depending on the user's task and needs, the long short-term memory neural network and the random forests are performing the best. The findings presented here facilitate and accelerate SMP data analysis and in consequence, help scientists to analyze the effects of climate change on the cryosphere more efficiently.