Co-Op, AI-Driven Equation Discovery

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.

• 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

• 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

• 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

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.

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.

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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.