Machine Learning Scientist - Molecular Force Fields

SandboxAQ, United States

The role

Introduction to the team: This role is for the PFAS team which sits within SandboxAQ's Chemical Simulation (ChemSim) group. Our mission is to develop PFAS-lean or PFAS-free substitutes and formulations for semiconductor process and fab materials that meet both performance specifications and environmental and safety requirements in complex semiconductor manufacturing environments. We combine generative ML, physics based simulation (e.g. DFT and molecular dynamics), Large Quantitative Models for property prediction, and multi-scale modeling with a partner driven experimental validation loop.

You will work alongside industrial co-development partners to move candidate molecules from prediction to qualified use.

What you will do

Own the training data. Design and run high-throughput physics based simulation campaigns that produce labelled training data for the PFAS chemical space, making the calls that determine whether the data is fit to train on — level of theory, basis set, convergence criteria, sampling strategy, and error budget.
Build the data generation infrastructure. Stand up automated, restartable, provenance tracked workflows across cloud and HPC by collaborating with the engineering team; curate and version the resulting datasets, and combine internally generated first-principles data with relevant public molecular and materials datasets under consistent conventions.
Train and own the MLFF stack for the PFAS vertical. Select architectures, train, validate, and deploy machine learned interatomic potentials across the chemical space of PFAS substitution demands. Fine tuning universal or foundation potentials, or training from scratch when appropriate.
Deliver the property predictions the screening pipeline runs on. Build and validate MLFF-driven protocols for the properties our pipelines need — for example protonation and deprotonation energetics underpinning pKa, ion diffusivity in glassy polymer matrices, and Green-Kubo viscosity and thermal conductivity — each benchmarked against reference data or experiment.
Push the potentials into the process chemistries that matter. Extend coverage to resin phase polymerisation and plasma surface etch chemistry, moving beyond single molecule, gas phase behaviour into the condensed phase regime our applications actually occupy.
Run production campaigns and deploy models on our platform. Execute at scale prediction campaigns across full candidate libraries, and land trained models, datasets, and reproducible training pipelines in SandboxAQ tooling so that other scientists can run them from written documentation.
Collaborate across the discovery loop, and set the honesty bar. Partner with the generative modeling, molecular property ML, and simulation teams so that MLFF-derived properties are usable as screening oracles and reward signals. Work with our partner facing materials scientists so predictions are checked against real process constraints. Report error bars, hold-out performance, and domain-of-validity limits alongside every predicted property, and escalate when a model is being asked a question it cannot answer.

What you need

Essential skills & experience

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 depth developing and deploying machine learned force fields — including at least one potential that you trained, validated, and put into the hands of other people who now rely on it, whether inside a company or as a public release. We are interested in what you have built and shipped rather than in years served, and this posting is open across experience levels.
Hands-on experience generating first principles training data at throughput with production quantum chemistry and electronic structure codes (e.g. VASP, Quantum ESPRESSO, CP2K, ORCA, Psi4, or Gaussian), including the judgment to set and defend level of theory, basis set, and convergence criteria.
Demonstrated fluency with descriptor based, modern equivariant, and message-passing potential architectures (e.g. MACE, NequIP, Allegro, TensorNet, DeePMD, ANI-class, or FairChem/UMA-family models) — including what each is good for and where each fails.
Strong programming skills in Python and hands on experience with modern ML frameworks (e.g. PyTorch, JAX), including training at scale on GPU clusters.
Practical experience coupling learned potentials into production molecular dynamics engines (e.g. LAMMPS, GROMACS, OpenMM, ASE) and extracting converged physical observables from the resulting trajectories.
Hands-on experience with classical molecular dynamics in those same engines — force field selection and parameterisation for organic and condensed phase systems (e.g. OPLS-AA, AMBER/GAFF, or ReaxFF for reactive chemistry), system construction and equilibration, ensemble and thermostat/barostat choice, and the judgment to say when a classical description stops being trustworthy and a learned potential is required.
Experience building automated computational workflows and curating large scientific datasets — provenance, versioning, and reproducibility.
Demonstrated validation discipline: a track record of characterising model error and out-of-domain failure rather than reporting best case benchmark numbers.
Experience working in cloud and high performance computing environments.

Highly desired skills & experience

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

WHY JOIN US?

We offer competitive compensation, a comprehensive benefits package, and opportunities for professional growth.

Compensation: Competitive base salary commensurate with experience, plus equity and performance-based incentives.
Benefits: Comprehensive health, dental, and vision insurance; 401(k) with company match; generous parental leave.
Work-Life Balance: Flexible hybrid work arrangements, generous PTO, and a culture that respects focus time and recovery.
Career Development: Direct exposure to CHIPS Act-funded programs, senior scientific and executive leadership, mentorship, and dedicated learning budgets to support continued growth.

Machine Learning Scientist - Molecular Force Fields

The role

Machine Learning Scientist - Molecular Force Fields

The role

What you will do

What you need

Essential skills & experience

Highly desired skills & experience

SandboxAQ welcomes all

About SandboxAQ

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