Computational Chemist - Machine Learned Force Fields, Material Discovery

What you'll do

• Generate and curate first-principles training data using automated, reproducible quantum chemistry and molecular dynamics workflows.
• Train, validate, and deploy force fields; benchmark property predictions and report model errors and domain-of-validity limits.
• Extend and run models for PFAS-substitution chemistries and screening campaigns, collaborating with scientific and engineering teams.

What they ask for

• PhD in Computational Chemistry, Materials Science, Physics, Machine Learning, or a related field, with deep specialization in machine learned interatomic potentials or atomistic simulation.
• Demonstrated experience developing and deploying machine learned force fields, including at least one trained, validated, and shipped potential.
• Experience generating first-principles training data with production quantum chemistry or electronic structure codes.
• Strong Python and modern ML framework skills; experience with GPU clusters, molecular dynamics engines, automated scientific workflows, and cloud or HPC environments.

Nice to have

• Experience with fluorine-containing chemistry, fluoropolymers, or PFAS-adjacent systems.
• Experience with free-energy or transport-property calculations, long-range electrostatics, polarization, implicit solvation, or generative molecular design.

Pay

$134.4K – $252K • Multiple Ranges (base salary; posting does not specify ISO currency code)

Tools

VASP, Quantum ESPRESSO, CP2K, ORCA, Psi4, Gaussian, Python, PyTorch

AI focus

Train, validate, deploy, and extend machine-learned interatomic potentials for material discovery and property prediction in PFAS substitution.