Chemical Physics (Enhanced Sampling, MLIPs) Bay Area preferred; exceptional remote candidates considered Full-Time Startup salary + substantial equity
Job Description:
We're looking for someone with strong expertise in molecular dynamics, enhanced sampling methods (e.g., metadynamics, 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.
What You'll Do:- 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
Who You Are:- 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.)Why Azulene LabsHigh-impact science with real-world consequences Significant equity as an early technical leader Deep-thinking, high-expectation environment
