The Vera C. Rubin Observatory is slated to observe nearly 20 billion galaxies during its decade-long Legacy Survey of Space and Time. The rich imaging data it collects will be an invaluable resource for probing galaxy evolution across cosmic time, characterizing the host galaxies of transient phenomena, and identifying novel populations of anomalous systems. To facilitate these studies, we introduce a convolutional variational autoencoder trained to rapidly estimate the redshift, stellar mass, and star-formation rate of galaxies from multi-band imaging data. We show that our CVAE can be used to identify physically-meaningful anomalies in large galaxy samples >100x faster than the leading parameter inference techniques.