If your background includes computational thermodynamics, molecular dynamics, enhanced sampling methods (such as metadynamics, umbrella sampling, replica exchange), force field design (including neural network potentials), free energy calculations, deep learning using PyTorch or similar, advanced analytical thermodynamics/statistical mechanics, and experience in electronic structure methods (such as DFT methods development)—we want to hear from you.

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What you’ll do:

• Lead or contribute to projects in molecular dynamics, enhanced sampling, free energy calculations, rare event simulation, solvation models, path integral molecular dynamics, coarse-grained modeling, statistical reweighting, force field and neural network potential development, and computational thermodynamics

• Develop, test, and deploy new molecular simulation techniques and high-accuracy force fields and neural network potentials

• Build and apply deep learning models for molecular and materials property prediction, using frameworks like PyTorch

• Write efficient, high-performance code (C++ and/or Python)

• Own your work from theory through prototyping and scalable deployment
  

Who you are:

• PhD in chemical physics, chemistry, applied physics, high-energy physics, materials science, or a related discipline

• Significant expertise in computational thermodynamics, molecular dynamics, enhanced sampling techniques (metadynamics, umbrella sampling, replica exchange), force field development (including neural network potentials), free energy calculations, deep learning for molecular systems (using PyTorch or TensorFlow), advanced statistical mechanics, electronic structure (e.g., DFT methods development), rare event sampling, solvation models, path integral molecular dynamics, and/or coarse-grained modeling

• Proficient in scientific programming with Python and/or C++; parallel tools (OpenMP, MPI, CUDA) are a plus

• Deep understanding of quantum mechanics, statistical mechanics, and thermodynamics as applied to molecular simulations

• Experience developing or applying deep learning models for scientific or simulation tasks (ideally with PyTorch or TensorFlow)

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Why Azulene Labs?

• Transformational impact: Crack fundamental bottlenecks in science, with direct consequences for health, climate, and technology

• Ownership at scale: Receive a significant equity stake as an early employee—your contribution directly shapes the company’s future and success

• Culture of excellence: Join a team that values deep thought, relentless learning, and tackling the hardest scientific problems head-on

• Real challenge: We work hard. The problems are tough, expectations are high, and the reward—personal, professional, and scientific—is extraordinary

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Location:

• Local candidates (San Francisco Bay Area): In-person preferred

• Remote considered for exceptional out-of-state candidates

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Compensation:
Startup-level salary plus a major equity grant as an early team member.

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Ready to redefine what’s possible in molecular simulation?
Please apply with your CV and a brief description of your most original analytical or computational accomplishments.