Computational Chemist – Machine Learned Force Fields, Material Discovery

SandboxAQ

. Design and run high-throughput quantum chemistry and molecular dynamics campaigns to produce training data for PFAS chemical space .

Posted 10/26/2026full-timeRemote • United StatesMid-LevelSenior💰 $134,400 - $252,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in designing and executing high-throughput quantum chemistry and molecular dynamics campaigns, with a strong focus on machine learned interatomic potentials and automated data-generation workflows. Proficient in deploying models and datasets in cloud and HPC environments while ensuring reproducibility and validation of predictions.

Highest-signal resume keywords

PhD In Computational ChemistryMachine Learned Interatomic PotentialsPython Programming SkillsExperience With VASP, Quantum ESPRESSO, CP2KHands-On Experience With PyTorch Or JAX

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Quantum ChemistryMolecular DynamicsMachine LearningData Generation WorkflowsForce Field DevelopmentDescriptor-Based Potential ArchitecturesAutomated Computational WorkflowsModel ValidationPhysical Observable ExtractionFree-Energy Calculations

Soft Skills

Judgment In Theory SelectionCollaboration With TeamsAttention To Detail

Tools & Technologies

VASPQuantum ESPRESSOCP2KPyTorchJAXLAMMPSGROMACSOpenMMSandboxAQ ToolingGaussian

Industry Keywords

PFAS ChemistryFluoropolymersGenerative Molecular DesignHigh-Performance ComputingMolecular Property ML

Tech Stack

Tools & technologies

CloudPythonPyTorch

About the role

Key responsibilities & impact

Design and run high-throughput quantum chemistry and molecular dynamics campaigns to produce training data for PFAS chemical space
Own reproducible, automated, restartable, provenance-tracked data-generation workflows across cloud and HPC
Curate and version datasets, combining internally generated first-principles data with relevant public molecular and materials datasets
Select architectures, train, validate, and deploy machine-learned interatomic potentials across PFAS substitution chemical space
Fine-tune universal or foundation potentials, or train models from scratch where appropriate
Build and validate MLFF-driven protocols for protonation/deprotonation energetics, ion diffusivity, viscosity, and thermal conductivity
Extend potentials to resin-phase polymerisation and plasma surface etch chemistry
Execute large-scale prediction campaigns across candidate libraries
Deploy trained models, datasets, and reproducible pipelines in SandboxAQ tooling with written documentation
Collaborate with generative modeling, molecular property ML, simulation, and materials science teams
Validate predictions against real process constraints and report error bars, hold-out performance, and domain-of-validity limits
Escalate when models are applied beyond their validated capabilities

Requirements

What you’ll need

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 trained, validated, and put into use by others
Hands-on experience generating first principles training data with production quantum chemistry and electronic structure codes such as VASP, Quantum ESPRESSO, CP2K, ORCA, Psi4, or Gaussian
Judgment to set and defend level of theory, basis set, and convergence criteria
Fluency with descriptor-based, modern equivariant, and message-passing potential architectures, including MACE, NequIP, Allegro, TensorNet, DeePMD, ANI-class, or FairChem/UMA-family models
Strong programming skills in Python
Hands-on experience with PyTorch or JAX, including training at scale on GPU clusters
Experience coupling learned potentials into molecular dynamics engines such as LAMMPS, GROMACS, OpenMM, or ASE
Experience extracting converged physical observables from molecular dynamics trajectories
Hands-on experience with classical molecular dynamics, force-field selection and parameterisation for organic and condensed-phase systems
Experience with OPLS-AA, AMBER/GAFF, or ReaxFF
Experience with system construction, equilibration, ensemble, thermostat, and barostat selection
Experience building automated computational workflows and curating large scientific datasets with provenance, versioning, and reproducibility
Demonstrated validation discipline, including characterising model error and out-of-domain failure
Experience working in cloud and high-performance computing environments
Highly desired: fluorine-containing chemistry, fluoropolymers, or PFAS-adjacent molecular systems
Highly desired: free-energy calculations, Green-Kubo or non-equilibrium molecular dynamics
Highly desired: long-range electrostatics, polarization, or implicit solvation treatments in learned potentials
Familiarity with generative molecular design frameworks

Benefits

Comp & perks

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
Senior scientific and executive leadership exposure
Mentorship
Dedicated learning budgets
Opportunities for professional growth