Co-Op, AI-Driven Equation Discovery

Lila Sciences, Inc.; Cambridge, San Francisco (MA, CA); On-site; USD 41,000 - 55,000; Part time

Job summary

Lila Sciences, Inc. in Cambridge is seeking a Co-Op focused on AI-driven equation discovery to develop and benchmark methods that extract interpretable governing equations from experimental data, enabling physics-informed models and digital twins.

You will work with PS AI researchers and scientists, building end-to-end pipelines, benchmarking symbolic regression, and translating open scientific questions into concrete ML tasks, with opportunities to publish and present findings.

Qualifications

• 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

Responsibilities

• 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

Skills

Python

PyTorch

JAX

Machine learning

Education

Master's or PhD in ML, CS, applied math, physics, materials science, or chemical engineering

Tools

PyTorch

JAX

Job description

Cambridge, MA USA; San Francisco, CA USA

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.

Lila Sciences iscommitted to equal employment opportunityregardless 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 .

By submitting this application, you acknowledge that Lila Sciences will process your personal data for recruitment purposes. We’ll retain your information for at least 12 months after the conclusion of the hiring process. For details on your rights and how we handle your data, see our Candidate Privacy Policy at lila.ai/candidate-privacy-policy-notice .