Designing effective cell therapies requires understanding how transcriptional regulation within infusion products influence patient outcomes. Here, we propose a generative model that leverages single-cell RNA sequencing (scRNA-seq) data paired with clinical outcomes to learn the gene regulatory networks within engineered Chimeric Antigen Receptor (CAR) T cells. Using conditional normalizing flows, our model captures the high-dimensional distribution of gene activity while conditioning on patient and response-specific features. This approach enables patient response prediction with 73\% accuracy and accurate simulations of gene knockdown, knockouts, and over-expression experiments. Our model identified function-recovering genetic modifications for CAR T infusion products, which were validated experimentally in the context of a genetic screen. SUBMISSION NUMBER: 43