--- title: 'Co-Op, AI-Driven Equation Discovery at Lila Sciences' canonical: 'https://feeny.ai/job/co-op-ai-driven-equation-discovery-lila-sciences-cambridge-hdvks2gddm7a' type: 'job' last_seen: '2026-11-06' --- # Co-Op, AI-Driven Equation Discovery at Lila Sciences - **Company:** Lila Sciences - **Location:** Cambridge, MA / San Francisco, CA - **Posted:** 2026-10-30 - **Last confirmed live:** 2026-11-06 - **Apply:** https://job-boards.greenhouse.io/lilasciences/jobs/4469152009 ## Job description ## Your Impact at LILA The PS AI team sits at the intersection of Physical Sciences R&D and AI/ML. Lila runs high-throughput experiments across electrochemistry, formulations, and advanced materials, generating rich datasets that demand more than black-box models. As a Co-Op focused on AI-driven equation discovery, you will develop and benchmark methods that extract interpretable governing equations directly from that data. Discovered equations reveal the underlying physics, make predictions explainable, and serve as mechanistic building blocks for digital twins and closed-loop optimization. You will work alongside PS AI researchers and physical scientists, helping shape how Lila turns experimental data into scientific understanding at scale. ## What You'll Be Building - Build end-to-end equation discovery pipelines from preprocessing through candidate generation and validation - Benchmark symbolic regression methods (SINDy, PDE-FIND, PySR, LLM-guided search) on Lila campaign data - Apply equation discovery to live electrochemistry, formulation, and materials datasets - Design evaluation harnesses measuring recovery rate, generalization, noise robustness, and physical consistency - Embed discovered equations into surrogate models, physics-informed models, and experimental digital twins - Translate open scientific questions into concrete problems with datasets, baselines, and success criteria - Document and present findings across PS AI and domain science teams ## What You'll Need to Succeed - Pursuing a Master's or PhD in ML, CS, applied math, physics, materials science, or chemical engineering - Strong Python skills with hands-on experience in PyTorch, JAX, or similar - Experience applying machine learning to messy, real-world scientific or experimental data - Solid grounding in linear algebra, calculus, and probability - Comfort reading and implementing methods from ML research papers - Ability to frame open-ended scientific questions as concrete ML tasks - Clear communication across research, engineering, and physical science teams Bonus Points For - Working knowledge of symbolic or sparse regression methods (SINDy, PySR, PDE-FIND, or equivalent) - Experience with LLM-guided or agentic symbolic regression (LLM-SR, SR-Scientist, or similar) - Background in physics-informed ML, uncertainty quantification, or operator learning (PINNs, neural ODEs, FNO, DeepONet) - Exposure to digital twin or closed-loop workflows: model calibration, active learning, Bayesian optimization - Domain knowledge in electrochemistry, formulations, superconductors, or materials science ## 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, Targeted Non-Viral Delivery](https://feeny.ai/job/co-op-targeted-non-viral-delivery-lila-sciences-cambridge-g7gc30z235rg) — Cambridge, MA - [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