About the role
Your Impact at LILA Lila Sciences is seeking an ML Scientist I/II, Nucleic Acid Design to advance RNA and DNA sequence design. This scientist will develop models and design strategies for understanding and engineering nucleic acid sequences, including problems such as 3’ UTR optimization, 5’ UTR optimization, CDS optimization, and promoter / enhancer design. You’ll work at the intersection of machine learning, sequence modeling, experimental design, and platform development. The work spans both applied design campaigns and building next-generation models that improve how Lila generates, evaluates, and learns from nucleic acid sequence-function data. The ideal candidate brings strong ML judgment, curiosity about biological mechanisms, and enthusiasm for areas such as regulatory genomics, RNA biology, and sequence-to-function modeling. You’ll collaborate with experimental scientists, ML researchers, and platform teams to build models that connect nucleic acid sequence design to biological function and make these capabilities usable across Lila’s autonomous science platform. What You'll Be Building Build ML models for RNA and DNA sequence design across regulatory and coding sequence contexts. Develop methods spanning de novo generation, sequence property prediction, diverse set selection for experimental validation, and active learning strategies. Deeply investigate the biological mechanisms of designed sequences and propose hypotheses about why they succeed or fail. Turn these insights into better models and future design principles. Partner with experimental scientists to propose informative assays, validation strategies, and learning loops. Collaborate with ML scientists and engineers across Lila to integrate nucleic acid design models into robust platforms and agent-driven frameworks. Stay current with research in nucleic acid biology, sequence design, and scientific ML, and share research findings externally through papers or blog posts. What You'll Need to Succeed PhD or equivalent experience in machine learning, computational biology, bioengineering, computer science, statistics, or a related quantitative field. Hands-on experience building, training, and evaluating ML models for DNA or RNA. Strong foundation in modern ML methods, with practical experience using frameworks such as PyTorch, JAX, or equivalent tools. Experience developing models for sequence design, sequence-function prediction, generative modeling, or active learning. Ability to reason about complex biological systems, scope ambiguous scientific problems, and formulate ML approaches that address difficult sequence-function challenges. Curiosity about nucleic acid biology, including RNA biology, regulatory genomics, or related sequence-to-function problems. Strong communication and collaboration skills, with a preference for team-based science and the ability to build shared technical direction across ML, engineering, platform, and experimental teams.
