AI Nucleic Acid Design Co-op

Your Impact at LILA

Lila Sciences is building scientific superintelligence by pairing large-scale automated experimentation with frontier machine learning. Within Lila, the Life Science AI (LSAI) function researches automated reasoning on biological data, combining state-of-the-art ML with breakthrough biology.

This co-op sits with the nucleic acid design group in LSAI, which builds models and design strategies for RNA and DNA sequences. You'll join our Nucleic Acid Design team for a 6-month co-op focused on open-ended research questions that could shape how Lila designs nucleic acid sequences in the future.

You'll work at the intersection of machine learning, sequence modeling and nucleic acid biology, on problems such as UTR design, regulatory element design, and using large reasoning models for scientific discovery. You'll own a defined research project with mentorship from ML scientists, and you'll work closely with experimental scientists to connect your models to real biological data.

The ideal candidate is curious, comfortable with open-ended problems, and excited to learn both the ML and biology behind sequence-to-function modeling.

What You'll Be Building

• Lead a scoped research project in nucleic acid sequence modeling or design.
• Build ML models on RNA and DNA sequence-function data, including prototyping new methods for property prediction, generative design or candidate selection.
• Investigate model behavior and propose biological hypotheses about why sequences succeed or fail.
• Explore how large reasoning models can drive scientific discovery and sequence design, for example by generating hypotheses, reasoning over experimental results or guiding design choices.
• Collaborate with experimental scientists to understand assays and to propose experiments that test new ideas.
• Present findings to the team and contribute to research write-ups, and where possible to external publications or presentations.

What You'll Need to Succeed

• Currently enrolled in an MS or PhD program in machine learning, computational biology, bioengineering, computer science, statistics or a related quantitative field.
• Hands-on experience building and training ML models, with strong Python skills and experience with PyTorch, JAX or a similar framework.
• Coursework or research experience with biological sequence data (DNA or RNA).
• Curiosity about nucleic acid biology, such as RNA biology, gene regulation or sequence-to-function relationships.
• Available for a full-time, 6-month co-op term onsite in San Francisco.

Bonus Points For

• Experience with sequence-to-function models, genomic language models, or models trained on MPRA, reporter assay or other functional genomics data.
• Familiarity with RNA secondary structure prediction, UTR or codon design, or inverse design methods.
• Experience with generative modeling, active learning or Bayesian optimization.
• Exposure to wet-lab data or collaboration with experimental scientists.
• A research publication, preprint or open-source project in ML or computational biology.
• Experience using or building with large language or reasoning models, AI agents, or LLM-based tools for scientific tasks.