We present implementation details, testing, and results from a new inversion‐based methodology, known colloquially as the “grand inversion,” developed for the Uniform California Earthquake Rupture Forecast (UCERF3). We employ a parallel simulated annealing algorithm to solve for the long‐term rate of all ruptures that extend through the seismogenic thickness on major mapped faults in California while simultaneously satisfying available slip‐rate, paleoseismic event‐rate, and magnitude‐distribution constraints. The inversion methodology enables the relaxation of fault segmentation and allows for the incorporation of multifault ruptures, which are needed to remove magnitude‐distribution misfits that were present in the previous model, UCERF2. The grand inversion is more objective than past methodologies, as it eliminates the need to prescriptively assign rupture rates. It also provides a means to easily update the model as new data become available. In addition to UCERF3 model results, we present verification of the grand inversion, including sensitivity tests, tuning of equation set weights, convergence metrics, and a synthetic test. These tests demonstrate that while individual rupture rates are poorly resolved by the data, integrated quantities such as magnitude–frequency distributions and, most importantly, hazard metrics, are much more robust.