Sequential Monte Carlo (SMC, particle filtering) has been used successfully to sample from Bayesian posterior distributions of phylogenetic trees. Our work seeks to apply SMC to the hierarchical Bayesian Multispecies Coalescent (MSC) model. In MSC, the primary distribution of interest is the marginal species tree posterior, which is challenging because the species tree is a hyperparameter that constrains the topology and node heights of the gene trees nested within and is thus only indirectly influenced by the observed sequence data. I will discuss our approach to applying SMC to the MSC model and illustrate with results from simulated as well as empirical datasets.