Ambiguities in data and problem constraints can lead to diverse, equally plausible outcomes for a machine learning task. In beat and downbeat tracking, for instance, different listeners may adopt various rhythmic interpretations, none of which would necessarily be incorrect. To address this, we propose a contrastive self-supervised pre-training approach that leverages multiple hypotheses about possible positive samples in the data. Our model is trained to learn representations compatible with different such hypotheses, which are selected with a knowledge-based scoring function to retain the most plausible ones. When fine-tuned on labeled data, our model outperforms existing methods on standard benchmarks, showcasing the advantages of integrating domain knowledge with multi-hypothesis selection in music representation learning in particular.