Chemical Physics; Enhanced Sampling, MLIPs

Bay Area preferred; exceptional remote candidates considered

Full-Time

Startup salary + substantial equity

We're looking for someone with strong expertise in molecular dynamics, enhanced sampling methods (e.g., meta dynamics, umbrella sampling, replica exchange), free energy calculations, computational thermodynamics, deep learning, and force-field / neural network potential development. Experience with electronic structure methods (DFT) and statistical mechanics is a major plus.

• • Lead or contribute to MD, enhanced sampling, and free energy projects
• • Develop and deploy high-accuracy force fields and neural network potentials
• • Build deep learning models for molecular and materials prediction (PyTorch or similar)
• • Write efficient scientific software in Python and/or C++
• • Take ideas from theory to scalable implementation

• • PhD in chemical physics, chemistry, physics, materials science, or related field
• • Strong background in MD, enhanced sampling, free energy methods, force-field/NN-potential development, and deep learning for molecular systems
• • Experience with electronic structure (DFT), statistical mechanics, or rare-event methods
• • Proficient in scientific programming; parallel tools a plus
• • Preferred:
  OpenMM, OpenFE, and training MLIPs (e.g., MACE, etc.)

• • High-impact science with real-world consequences
• • Significant equity as an early technical leader
• • Deep-thinking, high-expectation environment