The fast‐growing interests in high spatial resolution of seismic imaging and high temporal resolution of seismic monitoring pose great challenges for fast, efficient, and stable data processing in ambient‐noise seismology. This coincides with the explosion of available seismic data in the last few years. However, the current computational landscape of ambient seismic field seismology remains highly heterogeneous, with individual researchers building their own homegrown codes. Here, we present NoisePy—a new high‐performance python tool designed specifically for large‐scale ambient‐noise seismology. NoisePy provides most of the processing techniques for the ambient field data and the correlations found in the literature, along with parallel download routines, dispersion analysis, and monitoring functions. NoisePy takes advantage of adaptable seismic data format, a parallel input and output enabled HDF5 data format designed for seismology, for a structured organization of the cross‐correlation data. The parallel computing of NoisePy is performed using Message Passing Interface and shows a strong scaling with the number of cores, which is well suited for embarrassingly parallel problems. NoisePy also uses a small memory overhead and stable memory usage. Benchmark comparisons with the latest version of MSNoise demonstrate about four‐time improvement in compute time of the cross correlations, which is the slowest step of ambient‐noise seismology. NoisePy is suitable for ambient‐noise seismology of various data sizes, and it has been tested successfully at handling data of size ranging from a few GBs to several tens of TBs.