Body waves traversing the Earth’s interior from a seismic source to receivers on the surface carry rich information about its internal structures. Their travel time measurements have been widely used in seismology to constrain Earth’s interior at the global scale by mapping the time anomaly along their ray paths. However, picking the travel time of global seismic waves, suitable for studying Earth’s fine-scale structures, requires highly skilled personnel and is often fairly subjective. Here, we report the development of an automatic picker for PKIKP waves, traversing the Earth nearly along its diameters and through the inner core, based on the latest advances in supervised deep learning. A convolutional neural network (CNN) we developed automatically determines the PKIKP onset on vertical seismograms near its theoretical prediction of cataloged earthquakes. As high-quality manual onset picks of global seismic phases are limited, we employed a scheme to generate a synthetic supervised training dataset containing 300,000 waveforms. The PKIKP onsets picked by our trained CNN automatic picker exhibit a mean absolute error of ~0.5 s compared to 1,503 manual picks, comparable to the estimated human-picking error. In an integration test, the CNN automatic picks obtained from an extended waveform dataset yield a cylindrically anisotropic inner core model that agrees well with the models inferred from manual picks, which illustrates the success of this pilot model. This is a significant step closer to harvesting an unprecedented volume of travel time measurements for studying the inner core or other regions of the Earth’s deep interior.