--- title: 'Co-Op, AI Nucleic Acid Design at Lila Sciences' canonical: 'https://feeny.ai/job/co-op-ai-nucleic-acid-design-lila-sciences-san-francisco-jna9hpj3dsxn' type: 'job' last_seen: '2026-10-30' --- # Co-Op, AI Nucleic Acid Design at Lila Sciences - **Company:** Lila Sciences - **Location:** San Francisco, CA - **Posted:** 2026-10-27 - **Last confirmed live:** 2026-10-30 - **Apply:** https://job-boards.greenhouse.io/lilasciences/jobs/4465061009 ## Job description ## 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](https://www.lila.ai/candidate-privacy-policy-notice). 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. ## About Lila Sciences ## Company Overview - **One-liner**: Lila Sciences is building the world’s first Scientific Superintelligence platform and autonomous lab, using AI to autonomously generate hypotheses, design and run experiments, and learn from results in real time across life sciences, chemistry, materials, energy, and defense. - **Entity Type**: Private (Privately Held) – Series A, Seed, and Grant funding rounds - **Headquarters**: Cambridge, Massachusetts, United States - **Founded**: Not publicly available (first funding round was Seed in March 2025) - **Founders**: Not publicly available (founded within Flagship Pioneering’s ecosystem) ## Core Business - **Primary industry**: AI-powered scientific discovery and autonomous laboratory platforms - **Target customers**: B2B – research organizations, biotech, pharmaceutical, energy, materials, aerospace, and defense companies - **Mission**: “Scientific Superintelligence to solve humankind’s greatest challenges” – accelerating discovery across medicine, materials, energy, and defense ## Products & Services - **LILA Platform (Scientific Superintelligence)**: An AI model and operating system that autonomously executes the entire scientific method – generating hypotheses, designing experiments, running them in physical labs, and learning from results in real time. - **AI Science Factory™ Instruments**: Proprietary hardware and robotics that serve as the “body” of the platform, enabling automated experimentation at scale. - **Domain-specific applications**: Tailored solutions for biotech (drug discovery, protein engineering), therapeutics (mRNA, antibodies, cell therapies), energy & environment (clean fuels, catalysis, critical minerals), advanced materials, chemicals, aerospace & defense, and oil & gas. ## Market Standing - **Valuation**: Not disclosed - **Key Metric**: Total funding – USD $550.7M (as of LinkedIn data) - Seed Round (March 2025): $200M led by Flagship Pioneering - Series A (September 2025): $235M led by Braidwell and Collective Global Management - Series A (November 2025): $115M led by NVentures (NVIDIA) - Grant (January 2026): $671,400 led by ARIA - **Notable Investors/Partners**: Flagship Pioneering, NVIDIA (NVentures), Braidwell, Collective Global Management, ARIA - **Growth Signals**: Named #25 on the 2026 CNBC Disruptor 50 List; headcount of 308 employees (monthly growth +10.6%); 122 active job postings; operates in 6 countries (US, UK, Canada, Poland, Spain, Germany) ## Competitive Advantages - **Proprietary AI model** that consistently outperforms other models across scientific domains in complex analysis and reasoning. - **Autonomous physical labs** that close the loop between AI hypothesis generation and real-world experimentation. - **“Team of Teams” operating model** enabling startup speed at scale while maintaining radical transparency and high trust. - **General platform approach** (inspired by Rich Sutton’s “Bitter Lesson”) rather than narrow domain-specific tools, allowing broad applicability. ## Strategic Focus - Accelerating discovery across medicine, materials, energy, and defense - Scaling the autonomous science platform to more industries and use cases - Building “Scientific Superintelligence” that can tackle humanity’s greatest challenges - Continued investment in AI research, robotics, and lab automation ## Why Work Here - **Culture**: Emphasizes velocity, trust, curiosity, truth, and grit. “Think freely, prove precisely.” A high-trust, mission-driven environment where scientists and engineers work side by side. - **Remote/Hybrid/Office**: Roles are listed in Cambridge, MA; San Francisco, CA; and London, UK. Physical lab presence suggests significant on-site work, but some roles may offer flexibility. Policy not explicitly stated. - **Notable perks/engineering culture**: Opportunity to work at the frontier of AI and scientific discovery; collaboration with world-renowned experts; access to cutting-edge robotics and AI infrastructure; strong emphasis on learning and teaching (“generous teachers and eager learners”). ## Sources 1. [lila.ai](https://www.lila.ai/) 2. [lila.ai/about](https://www.lila.ai/about) 3. [lila.ai/open-roles](https://www.lila.ai/open-roles) 4. [LinkedIn - Lila Sciences](https://www.linkedin.com/company/lila-sciences) 5. [Greenhouse Job Board](https://job-boards.greenhouse.io/lilasciences/jobs/4246302009) ## Other roles at Lila Sciences - [Co-Op, Editorial Content](https://feeny.ai/job/co-op-editorial-content-lila-sciences-cambridge-wxs4v4cxc0av) — Cambridge, MA - [Co-Op, Bayesian Methods for Complex Decision Making](https://feeny.ai/job/co-op-bayesian-methods-for-complex-decision-making-lila-sciences-cambridge-ndt2w02kgvj2) — Cambridge, MA / San Francisco, CA - [Co-Op, SCADA Engineer](https://feeny.ai/job/co-op-scada-engineer-lila-sciences-cambridge-hv28gbacp62z) — Cambridge, MA - [Co-Op, Structuring Scientific Data for Discovery](https://feeny.ai/job/co-op-structuring-scientific-data-for-discovery-lila-sciences-cambridge-57tgh64e8y0k) — Cambridge, MA / San Francisco, CA - [Co-Op, Electron Microscopy](https://feeny.ai/job/co-op-electron-microscopy-lila-sciences-cambridge-nj1aaxqp55xm) — Cambridge, MA - [Co-Op, Finance](https://feeny.ai/job/co-op-finance-lila-sciences-cambridge-8p0szx7ty94c) — Cambridge, MA - [Associate Director/Director, Frontier and AI Product Counsel](https://feeny.ai/job/associate-director-director-frontier-and-ai-product-counsel-lila-sciences-7z94cshd3d66) — Cambridge, MA / San Francisco, CA - [Co-Op, Robot Perception & State Estimation](https://feeny.ai/job/co-op-robot-perception-state-estimation-lila-sciences-cambridge-yznrym7yenxa) — Cambridge, MA - [Co-Op, Reaction Prediction & Benchmarking](https://feeny.ai/job/co-op-reaction-prediction-benchmarking-lila-sciences-cambridge-yc1ec2e9sfb4) — Cambridge, MA / San Francisco, CA - [Co-Op, Agentic Simulations for Materials Science](https://feeny.ai/job/co-op-agentic-simulations-for-materials-science-lila-sciences-cambridge-z1m0175gttp2) — Cambridge, MA / San Francisco, CA