Protein engineering — the design of protein variants with desirable properties — is a central pursuit in biotechnology. In therapeutic discovery, after a promising antibody candidate has been found, it is often necessary to reduce immunogenicity, eliminate aggregation or increase plasma half-life while preserving binding affinity. In synthetic biology, engineered enzymes — for example, PETases that can rapidly degrade plastic, or designed enzymes that can catalyse new reactions — can be improved by increasing thermal stability and enhancing expressibility while conserving, or even boosting, catalytic efficiency. These pursuits have traditionally been carried out experimentally, either by rationally designing mutations, or with directed evolution, techniques which are limited to a small number of tested variants. In recent years, novel computational tools have arisen that can screen hundreds of thousands or millions of variants in short times. I am interested in progressing this field by developing multimodal deep learning methods, which incorporate diverse sources of biological information, to deliver the next generation of protein engineering algorithms.