--- title: 'Co-Op, Targeted Non-Viral Delivery at Lila Sciences' canonical: 'https://feeny.ai/job/co-op-targeted-non-viral-delivery-lila-sciences-cambridge-g7gc30z235rg' type: 'job' last_seen: '2026-11-06' --- # Co-Op, Targeted Non-Viral Delivery at Lila Sciences - **Company:** Lila Sciences - **Location:** Cambridge, MA - **Posted:** 2026-11-02 - **Last confirmed live:** 2026-11-06 - **Apply:** https://job-boards.greenhouse.io/lilasciences/jobs/4472544009 ## Job description ## Your Impact at LILA Lila is seeking a co-op to help build the automated non-viral delivery platform. The role focuses on improving an automated system for production and characterization of targeted lipid nanoparticles (tLNP). Day-to-day work involves producing tLNPs to support ongoing experiments, developing new conjugation and purification methods for tLNPs, and applying those methods on the automated system. The co-op works alongside chemists, biologists, automation engineers, and data scientists, with hands-on time across both bench science and automated lab systems. ## What You'll Be Building - Formulate and characterize targeted LNPs on an automated microfluidic workcell - Run high-throughput screens of ionizable lipids and targeting ligands - Execute downstream characterization assays such as DLS, RiboGreen, and TNS - Onboard new workflows and experiment types onto the workcell - Test and document changes that increase screening throughput - Record methods, results, and observations in the ELN for reuse across teams ## What You'll Need to Succeed - Pursuing a BSc or MSc in chemical engineering, biomedical engineering, pharmaceutical sciences, biology, or a related field - Hands-on wet-lab experience and comfort following detailed SOPs - Data analysis skills, with the ability to interpret results and iterate on experimental design - Clear written and verbal communication, including ELN documentation - Available for the full January co-op term, onsite at Alewife Bonus Points For - Experience with lipid nanoparticle formulation, microfluidics, or other drug delivery systems - Familiarity with automated systems or high-throughput screening platforms - Familiarity with protein characterization methods - Working knowledge of bioanalytical techniques such as fluorescence spectroscopy and electrophoresis ## 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 - [Director / Senior Director, Intellectual Property, Life Sciences](https://feeny.ai/job/director-senior-director-intellectual-property-life-sciences-lila-sciences-663mx8sk9dpg) — Cambridge, MA / San Francisco, CA - [Automation Engineer I/II, Sustaining Engineering & Lab Operations](https://feeny.ai/job/automation-engineer-i-ii-sustaining-engineering-lab-operations-lila-sciences-2wp1t59bbm9s) — Cambridge, MA - [Co-op, Agentic and Generative Protein Design](https://feeny.ai/job/co-op-agentic-and-generative-protein-design-lila-sciences-cambridge-avtbwmeq8bq9) — Cambridge, MA / San Francisco, CA - [Co-Op, AI-Driven Equation Discovery](https://feeny.ai/job/co-op-ai-driven-equation-discovery-lila-sciences-cambridge-hdvks2gddm7a) — Cambridge, MA / San Francisco, CA - [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