Radio Frequency Interference (RFI) is a massive problem for radio observatories around the world. Due to the growth of telecommunications and air travel RFI is increasing exactly when the world's radio telescopes are increasing significantly in sensitivity, making RFI one of the most pressing problems for astronomy in the era of the Square Kilometre Array (SKA). Traditionally RFI is dealt with through simple algorithms that remove unexpected rapid changes but the recent explosion of machine learning and artificial intelligence (AI) provides an exciting opportunity for pushing the state-of-the-art in RFI excision. Unfortunately, due to the lack of training data for which the true RFI contamination is known, it is impossible to reliably train and compare machine learning algorithms for RFI excision on radio telescope arrays currently. To address this stumbling block we present RFIsim, a radio interferometry simulator that includes the telescope properties of the MeerKAT array, a sky model based on previous radio surveys coupled with an RFI model designed to reproduce actual RFI seen at the MeerKAT site. We perform an indepth comparison of the simulator results with real observations using the MeerKAT telescope and show that RFIsim produces visibilities that mimic those produced by real observations very well. Finally, we describe how the data was key in the development of a new state-of-the-art deep learning RFI flagging algorithm in Vafaei et al. (2020.) [69] In particular, this work demonstrates that transfer learning from simulation to real data is an effective way to leverage the power of machine learning for RFI flagging in real-world observatories.