ML Scientist I/II, Nucleic Acid Design

Build and evaluate machine-learning models for RNA and DNA sequence design, covering regulatory and coding contexts. Develop methods for de novo generation, sequence property prediction, diverse set selection, and active learning to drive experimental validation. Collaborate with wet-lab scientists and ML/engineering teams to create learning loops and integrate models into Lila’s autonomous science platform, improving how the system generates and learns from sequence-function data.