Co-Op, AI Nucleic Acid Design

Lila Sciences • San Francisco, CA

3d • Onsite

About The Position

Lila Sciences is building scientific superintelligence by combining automated experimentation with machine learning. This six-month co-op is part of the Life Science AI function’s nucleic acid design group, which develops models and design strategies for RNA and DNA sequences. The co-op will focus on open-ended research that may shape how Lila designs nucleic acid sequences, working at the intersection of machine learning, sequence modeling, and nucleic acid biology. Research topics may include UTR design, regulatory element design, and using large reasoning models for scientific discovery.

The co-op student will own a defined research project with mentorship from ML scientists and collaborate closely with experimental scientists to connect computational models with biological data. The position is onsite in San Francisco and requires availability for a full-time, six-month co-op term.

Requirements

• 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 machine learning models.
• Strong Python skills and experience with PyTorch, JAX, or a similar framework.
• Coursework or research experience with biological sequence data, including DNA or RNA.
• Curiosity about nucleic acid biology, such as RNA biology, gene regulation, or sequence-to-function relationships.
• Available for a full-time, six-month co-op term onsite in San Francisco.

Nice To Haves

• 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 machine learning or computational biology.
• Experience using or building with large language or reasoning models, AI agents, or LLM-based tools for scientific tasks.

Responsibilities

• Lead a scoped research project in nucleic acid sequence modeling or design.
• Build machine learning models using RNA and DNA sequence-function data, including prototyping 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 support scientific discovery and sequence design, including generating hypotheses, reasoning over experimental results, or guiding design choices.
• Collaborate with experimental scientists to understand assays and propose experiments that test new ideas.
• Present findings to the team and contribute to research write-ups and, where possible, external publications or presentations.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern