Computational Chemist - Machine Learned Force Fields, Material Discovery
SandboxAQ · United States · remote
USD 134,400 to 252,000 per year
ABOUT SANDBOXAQ
SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.
We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.
At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.
THE OPPORTUNITY
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.
Introduction to the role: The PFAS team is hiring Machine Learning Scientists to own machine learned force fields (MLFFs) for the PFAS substitution programme, including generating the first principles data they are trained on. Machine learned potentials are the load bearing element of our discovery pipeline: the properties that decide whether a candidate molecule can replace a PFAS incumbent — among them reactivity, thermal stability, and the diffusivity through polymer matrices — are ones that the physics based simulations can compute accurately but far too slowly to screen a library, and that classical force fields compute quickly but may not be accurate enough to trust. In this role you will: (1) design and run the high-throughput quantum chemistry and molecular dynamics campaigns that produce our training data, and own the infrastructure that makes them reproducible; (2) train, validate, and deploy the potentials that data supports, and identify where each model's domain of validity ends; and (3) extend machine learned potentials into the chemistry that PFAS substitution requires.
See how SandboxAQ is helping build America's semiconductor supply chain from the materials up https://www.sandboxaq.com/post/sandboxaq-helping-build-americas-semiconductor-supply-chain
KEY RESPONSIBILITIES
- 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.
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