Pretraining deep language models has led to large performance gains in NLP. Despite this success, Schick and Schu ̈tze (2020) recently showed that these models struggle to under- stand rare words. For static word embeddings, this problem has been addressed by separately learning representations for rare words. In this work, we transfer this idea to pretrained language models: We introduce BERTRAM, a powerful architecture based on BERT that is capable of inferring high-quality embeddings for rare words that are suitable as input rep- resentations for deep language models. This is achieved by enabling the surface form and con- texts of a word to interact with each other in a deep architecture. Integrating BERTRAM into BERT leads to large performance increases due to improved representations of rare and medium frequency words on both a rare word probing task and three downstream tasks.