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Biotechnology, Life Sciences, Research

Co-Op, AI Nucleic Acid Design

San Francisco, CAOnsiteInternshipPosted 11 days ago

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Required skills

  • AI
  • Nucleic Acid Design
  • RNA
  • DNA
  • sequence modeling
  • machine learning
  • ML scientists
  • Python
  • PyTorch
  • JAX

Nice to have

  • sequence-to-function models
  • genomic language models
  • MPRA
  • reporter assay
  • functional genomics data
  • RNA secondary structure prediction

Role summary

Lila Sciences is seeking a Co-Op AI Nucleic Acid Design researcher for a 6-month full-time term in San Francisco. This role involves working at the intersection of machine learning, sequence modeling, and nucleic acid biology to build models and design strategies for RNA and DNA sequences. The co-op will lead a defined research project, build ML models for property prediction and generative design, investigate model behavior, and collaborate with experimental scientists. The ideal candidate is pursuing an MS or PhD in a quantitative field, has strong Python and ML framework experience (PyTorch, JAX), and coursework or research experience with biological sequence data. Curiosity about nucleic acid biology is essential.

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.

    • About LILA

    Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

    LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

    Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

    We’re All In

    Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

    Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

    A Note to Agencies

    Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

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