Remote ML Scientist: Molecular Force Fields

SandboxAQ; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

SandboxAQ is seeking a mid-level Machine Learning Scientist to own ML force fields for PFAS substitution within the ChemSim group. The role involves designing high-throughput quantum chemistry and MD campaigns, training ML interatomic potentials, and extending them into PFAS-related chemistries.

You will build reproducible pipelines, validate models across large chemical spaces, and deploy trained models on SandboxAQ tooling so scientists can reuse workflows and data in production environments.

Qualifications

• PhD in Computational Chemistry, Materials Science, Physics, Machine Learning, or a related field with deep specialization in ML interatomic potentials.
• Experience deploying ML force fields and shipping them for use inside a company or publicly.
• Hands-on data generation for first-principles training at scale with quantum chemistry codes.
• Fluency with descriptor-based, equivariant, and message-passing potentials; know strengths and limits.
• Strong Python programming and modern ML framework experience, including GPU training.
• Experience integrating learned potentials with production MD engines and extracting observables.
• Experience building automated workflows and curating large scientific datasets with provenance.
• Proven validation discipline and handling of out-of-domain cases; not only best-case benchmarks.
• Experience in cloud and HPC environments.
• Hands-on experience with fluorine-containing chemistry, fluoropolymers, or PFAS-adjacent systems.

Responsibilities

• Own the training data: design high-throughput physics campaigns and set theory level, basis, convergence, and sampling criteria.
• Build data generation infrastructure: automate, restartable workflows across cloud/HPC; curate datasets.
• Train and own MLFF stack: choose architectures, train, validate, and deploy potentials across PFAS space.
• Deliver property predictions: develop MLFF-driven protocols for protonation, diffusivity, viscosity, and thermal conductivity.
• Extend potentials to PFAS substitution chemistries: resin polymerisation and plasma surface chemistry.
• Run production campaigns and deploy models: scale predictions and publish documentation for reuse.

Skills

Python programming

Machine learning

Molecular simulation

High performance computing

Cloud computing

Education

PhD in Computational Chemistry/Materials Science/Physics/ML

Tools

VASP

Quantum ESPRESSO

CP2K

Gaussian

LAMMPS

GROMACS

OpenMM

ASE

PyTorch

JAX

Job description

SandboxAQ is seeking a mid-level Machine Learning Scientist to own ML force fields for PFAS substitution within the ChemSim group. The role involves designing high-throughput quantum chemistry and MD campaigns, training ML interatomic potentials, and extending them into PFAS-related chemistries.

You will build reproducible pipelines, validate models across large chemical spaces, and deploy trained models on SandboxAQ tooling so scientists can reuse workflows and data in production environments.