Chemical Physics (Enhanced Sampling, MLIPs)

Azulenelabs; San Francisco (CA); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Substantial equity

High-impact science

Deep-thinking environment

Job summary

A biotech startup in California is seeking a Chemical Physics expert to lead projects in molecular dynamics and enhanced sampling. The ideal candidate will have a PhD and strong expertise in deep learning, computational thermodynamics, and developing neural network potentials. This role offers substantial equity and the opportunity to engage in high-impact scientific work that has real-world consequences.

Qualifications

• Strong expertise in molecular dynamics and enhanced sampling methods.
• Experience with deep learning and computational thermodynamics.
• Proficient with DFT and statistical mechanics methods.
• Proficient in scientific programming; parallel tools a plus.

Responsibilities

• Lead or contribute to MD, enhanced sampling, and free energy projects.
• Develop and deploy accurate force fields and neural network potentials.
• Build deep learning models for molecular and materials prediction.
• Write efficient scientific software in Python and/or C++.

Skills

Molecular dynamics

Enhanced sampling methods

Deep learning

Free energy calculations

Scientific programming

C++

OpenMM

Education

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

Tools

Python

C++

PyTorch

C++

Job description

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 Labs

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