Sign language understanding remains a significant challenge, particularly for low-resource sign languages with limited annotated data. Motivated by the success of large-scale pretraining in deep learning, we propose Multi-Stream Masked Autoencoder (MS-MAE) — a simple yet effective framework for learning sign language representations from skeleton-based video data. We pretrained a model with MS-MAE on the YouTube-ASL dataset, and then adapted it to multiple downstream tasks across different sign languages. Experimental results show that MS-MAE achieves competitive or superior performance on a range of isolated sign language recognition benchmarks and gloss-free sign language translation tasks across several sign languages. These findings highlight the potential of leveraging large-scale, high-resource sign language data to boost performance in low-resource sign language scenarios. Additionally, visualization of the model’s attention maps reveals its ability to cluster adjacent pose sequences within a sentence, some of which align with individual signs, offering insights into the mechanisms underlying successful transfer learning.