Antibodies have become prominent therapeutic agents but are costly to develop. Existing approaches to predict developability depend on structure, which requires extensive laboratory or computational work to obtain. To address this issue, we developed a machine learning model to predict developability from sequence alone by extracting physicochemical and learned embedding features. Our model achieves high sensitivity and specificity on a dataset of 2,400 antibodies. These results suggest that sequence is predictive of developability, enabling more efficient development of antibodies.