The two principle earthquakes of the July 2019 Ridgecrest, California, earthquake sequence, MW 6.4 and 7.1, and their immediate foreshocks and thousands of aftershocks present a challenging environment for rapid analysis and characterization of this sequence as it unfolded. In this study, we analyze the first 6 days of the sequence using continuous data from available seismic networks to detect and locate earthquakes associated with the earthquake sequence. We build a high‐precision earthquake catalog using a deep‐neural‐network‐based picker—PhaseNet and a sequential earthquake association and location workflow. Without prior information, we automatically detect and locate more than twice as many earthquakes as the routine catalog. Our high‐precision earthquake catalog reveals detailed spatiotemporal evolution of the earthquake sequence and clearly defines multiple faults activated during the sequence. Our study demonstrates that it is possible to characterize earthquake sequences from raw seismic data using a well‐trained machine‐learning picker and our workflow. Plain Language Summary We build a high‐precision earthquake catalog for the July 2019 Ridgecrest, California, earthquake sequence from 4 July 2019 to 9 July 2019 without prior information using machine‐learning phase picks and a sequential earthquake association and location workflow. Our result is totally independent of the routine catalog and enables us to characterize the earthquake sequence starting from raw seismic data. Our high‐precision earthquake catalog reveals detailed spatiotemporal evolution of the earthquake sequence and clearly defines multiple faults activated during the sequence. Key Points Seismic P and S phases are obtained with a recent machine‐learning phase picker—PhaseNet We build a high‐precision earthquake catalog for the July 2019 Ridgecrest, California, earthquake sequence from raw seismic data Our high‐precision earthquake catalog reveals detailed seismicity evolution and seismic fault structures