Computational Chemist - Machine Learned Force Fields, Material Discovery

The role

You will own machine learned force fields for the PFAS substitution programme. This includes designing high-throughput quantum chemistry and molecular dynamics campaigns to produce training data and deploying machine learned potentials.

What you will do

• Design and run high-throughput physics based simulation campaigns to produce labelled training data.
• Build automated, restartable, provenance tracked workflows across cloud and HPC.
• Curate and version datasets by combining internal data with public datasets.
• Train, validate, and deploy machine learned interatomic potentials.
• Build and validate MLFF-driven protocols for properties like pKa, ion diffusivity, viscosity, and thermal conductivity.
• Extend coverage to resin phase polymerisation and plasma surface etch chemistry.
• Execute production prediction campaigns across candidate libraries.
• Land trained models and reproducible training pipelines in SandboxAQ tooling.
• Partner with generative modeling, molecular property ML, and simulation teams.
• Work with partner facing materials scientists to check predictions against process constraints.

Required qualifications

• PhD in Computational Chemistry, Materials Science, Physics, Machine Learning, or a related field.
• Deep specialization in machine learned interatomic potentials or atomistic simulation.
• Demonstrated depth developing and deploying machine learned force fields.
• Hands-on experience generating first principles training data with production quantum chemistry and electronic structure codes.
• Demonstrated fluency with descriptor based, modern equivariant, and message-passing potential architectures.
• Strong programming skills in Python.
• Hands on experience with modern ML frameworks such as PyTorch or JAX.
• Practical experience coupling learned potentials into production molecular dynamics engines.
• Hands-on experience with classical molecular dynamics in engines like LAMMPS, GROMACS, OpenMM, or ASE.
• Experience building automated computational workflows and curating large scientific datasets.
• Demonstrated validation discipline regarding model error and out-of-domain failure.
• Experience working in cloud and high performance computing environments.

Nice to have

• Hands-on experience with fluorine containing chemistry, fluoropolymers, or PFAS-adjacent molecular systems.
• Experience computing free energies by thermodynamic cycle or alchemical methods, and/or transport properties by Green-Kubo or non-equilibrium MD.
• Experience with long range electrostatics, polarization, or implicit solvation treatments in learned potentials.
• Familiarity with generative molecular design frameworks

Benefits

• Competitive base salary commensurate with experience, plus equity and performance-based incentives.
• Comprehensive health, dental, and vision insurance.
• 401(k) with company match.
• Generous parental leave.
• Flexible hybrid work arrangements.
• Generous PTO.
• Culture that respects focus time and recovery.
• Direct exposure to CHIPS Act-funded programs.
• Senior scientific and executive leadership.
• Mentorship.
• Dedicated learning budgets.

About the company

SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022.