Remote MLFF Chemist for PFAS Materials Discovery

SandboxAQ; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

SandboxAQ is seeking a Computational Chemist to lead machine learned force fields for PFAS substitution in the ChemSim group. The role focuses on generating training data via high-throughput quantum chemistry, training ML potentials, and deploying them in scalable workflows.

You will design campaigns, validate models, and extend ML potentials to complex chemistries relevant to PFAS research, contributing to semiconductor materials discovery and environmental safety goals.

Qualifications

• PhD in relevant field with strong focus on ML interatomic potentials.
• Proven experience training and deploying ML force fields.
• Experience generating first-principles data and using quantum chemistry codes.
• Hands-on experience with cloud/HPC workflows.

Responsibilities

• Own the training data: design and run high-throughput physics campaigns for labelled data.
• Build data generation infrastructure across cloud/HPC with provenance tracking.
• Train and own the MLFF stack for PFAS substitution and deploy models.
• Deliver property predictions for screening pipelines and benchmark against data/experiments.
• Extend potentials to resin polymerisation and plasma surface chemistry.
• Run production campaigns and deploy models on SandboxAQ tooling for reuse by other scientists.

Skills

Python programming

Machine learning

Molecular dynamics

Data pipelines

Education

PhD in Computational Chemistry, Materials Science, Physics, ML

Tools

VASP

Quantum ESPRESSO

CP2K

LAMMPS

PyTorch

GROMACS

Job description

SandboxAQ is seeking a Computational Chemist to lead machine learned force fields for PFAS substitution in the ChemSim group. The role focuses on generating training data via high-throughput quantum chemistry, training ML potentials, and deploying them in scalable workflows.

You will design campaigns, validate models, and extend ML potentials to complex chemistries relevant to PFAS research, contributing to semiconductor materials discovery and environmental safety goals.