--- title: 'Co-Op, Electron Microscopy at Lila Sciences' canonical: 'https://feeny.ai/job/co-op-electron-microscopy-lila-sciences-cambridge-nj1aaxqp55xm' type: 'job' last_seen: '2026-10-30' --- # Co-Op, Electron Microscopy at Lila Sciences - **Company:** Lila Sciences - **Location:** Cambridge, MA - **Posted:** 2026-10-28 - **Last confirmed live:** 2026-10-30 - **Apply:** https://job-boards.greenhouse.io/lilasciences/jobs/4469144009 ## Job description ## Your Impact at LILA Lila Sciences is seeking a Co-Op, Electron Microscopy to join the Materials Science team within the Autonomous Science Platform. This co-op supports advanced SEM characterization workflows across powdered materials and thin films, working alongside synthesis, characterization, and ML scientists to turn physical experiments into high-quality data. The work is hands-on and instrument-heavy: acquiring imaging and compositional data, refining methods, and iterating on protocols that feed Lila's materials discovery platform. It suits a PhD student who wants depth in microstructural analysis and sees how characterization data drives AI-guided materials research. ## What You'll Be Building - Design and execute SEM-based characterization workflows across diverse chemistries and form factors - Operate electron microscopy instrumentation under the guidance of staff scientists - Partner with experimental scientists and engineers to optimize protocols and inform downstream analysis - Troubleshoot instrumentation and workflow issues that affect data quality - Capture imaging and analysis results in clear, reusable laboratory records - Follow safety and compliance standards for instrument and sample handling ## What You'll Need to Succeed - Currently enrolled in a PhD in Materials Science, Physics, or a related field - Strong background in SEM-based microstructural analysis - Hands-on expertise with EDS, EBSD, and FIB (focused ion beam) - Experience characterizing powdered materials and thin films - Demonstrated ability to operate and troubleshoot advanced SEM systems - Strong organizational skills and attention to detail Bonus Points For - Experience with image analysis tools such as ImageJ or Python-based pipelines - Familiarity with complementary techniques such as XRD or XRF - Interest in automation and integration of microscopy with AI workflows - Prior work characterizing nanoscale defects and interfaces ## 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, 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, AI Nucleic Acid Design](https://feeny.ai/job/co-op-ai-nucleic-acid-design-lila-sciences-san-francisco-jna9hpj3dsxn) — San Francisco, CA - [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