Co-Op, AI-Driven Equation Discovery

Location: Cambridge, MA, USA or San Francisco, CA, USA

Company

hirify.global builds AI systems and proprietary instruments intended to automate the scientific method and accelerate discovery across medicine, materials, and energy.

What you will do

• Build end-to-end equation discovery pipelines covering preprocessing, candidate generation, and validation.
• Benchmark symbolic regression methods including SINDy, PDE-FIND, PySR, and LLM-guided search on experimental campaign data.
• Apply equation discovery to electrochemistry, formulation, and advanced materials datasets.
• Design evaluation harnesses for recovery rate, generalization, noise robustness, and physical consistency.
• Embed discovered equations into surrogate models, physics-informed models, and experimental digital twins.
• Define concrete datasets, baselines, and success criteria for open scientific questions; document and present findings across AI, research, engineering, and physical science teams.

Requirements

• Pursuing a Master’s or PhD in machine learning, computer science, applied mathematics, physics, materials science, or chemical engineering.
• Strong Python skills with hands-on experience in PyTorch, JAX, or similar tools.
• Experience applying machine learning to messy, real-world scientific or experimental data.
• Solid grounding in linear algebra, calculus, and probability.
• Ability to read and implement methods from machine learning research papers.
• Ability to frame open-ended scientific questions as concrete machine learning tasks.
• Clear communication across research, engineering, and physical science teams.

Nice to have

• Working knowledge of symbolic or sparse regression methods such as SINDy, PySR, or PDE-FIND.
• Experience with LLM-guided or agentic symbolic regression, including LLM-SR or SR-Scientist.
• Background in physics-informed machine learning, uncertainty quantification, or operator learning, including PINNs, neural ODEs, FNO, or DeepONet.
• Exposure to digital twin or closed-loop workflows such as model calibration, active learning, or Bayesian optimization.
• Domain knowledge in electrochemistry, formulations, superconductors, or materials science.

Culture & Benefits

• Work alongside physical sciences AI researchers and physical scientists at the intersection of AI/ML and experimental R&D.
• Startup-speed environment focused on truth, trust, curiosity, grit, and velocity.
• Equal employment opportunity regardless of protected characteristics.