LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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...
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