Chemical Physics (Enhanced Sampling, MLIPs)

MixMode; San Francisco (CA); On-site; USD 90,000 - 130,000; Full time

Benefits offered by this job

Significant equity

High-impact science

Collaborative and challenging environment

Job summary

MixMode is seeking a highly skilled molecular dynamics expert to lead and contribute to projects involving enhanced sampling and free energy calculations. The ideal candidate holds a PhD in a relevant field and has strong experience with force-field/neural network potential development, deep learning, and scientific programming. The position offers significant equity as an early technical leader in a high-expectation environment, making a tangible impact in the field.

Qualifications

• PhD in chemical physics, chemistry, physics, materials science, or related field.
• Strong background in MD, enhanced sampling, free energy methods, and deep learning.
• Experience with electronic structure methods (DFT) or statistical mechanics.
• Proficient in scientific programming; parallel tools a plus.

Responsibilities

• 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.
• Write efficient scientific software in Python and/or C++.
• Take ideas from theory to scalable implementation.

Skills

Molecular dynamics

Enhanced sampling methods

Free energy calculations

Deep learning

Force-field/neural network potential development

Scientific programming

Education

PhD in chemical physics, chemistry, physics, or materials science

Tools

Python

C++

OpenMM

OpenFE

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 Labs

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