Functional neuroimaging techniques allow us to estimate functional networks that underlie cognition. However, these functional networks are often estimated at the group level and do not allow for the discovery of, nor benefit from, subpopulation structure in the data, that is, the fact that some recording sessions may be more similar than others. Here, we propose the use of embedding vectors (c.f. word embedding in Natural Language Processing) to explicitly model individual sessions while inferring networks across a group. This vector is effectively a "fingerprint" for each session, which can cluster sessions with similar functional networks together in a learnt embedding space. We apply this approach to estimate dynamic functional networks using a hierarchical Hidden Markov Model (HMM). We call this approach HIVE (HMM with Integrated Variability Estimation). Using simulated data, we show that HIVE can uncover true subpopulation structure and show improved performance over existing approaches. Using real magnetoencephalography data, we show the learnt embedding vectors (session fingerprints) reflect meaningful sources of variation across a population. Overall, HIVE provides a powrful new approach for modelling individual sessions while leveraging information available across an entire group.