The experimental design based on black-box optimization and batch recommendation has been increasingly used for the design of genetic sequences. We briefly outline our recent results on using Bayesian optimization to maximise gene expression in bacteria, where machine learning enabled us to discover a strong regulatory element. Using the Design-Build-Test-Learn (DBTL) workflow as a case study of how to effectively use machine learning in genomic sequence design, we argue that machine learning has tremendous potential in this area. Based on our experience, we discuss several opportunities and challenges that we have identified, and conclude with a call to action for more collaborations.