Computational Chemist – Machine Learned Force Fields, Material Discovery

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🔥 1 minute ago

🇺🇸 United States – Remote

💵 $134.4k - $252k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

👻 Ghost score 0%

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SandboxAQ

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2021

🤖 Artificial Intelligence

🔒 Cybersecurity

💊 Pharmaceuticals

Artificial Intelligence • Cybersecurity • Pharmaceuticals

SandboxAQ is a company developing quantitative AI and quantum-inspired technologies to solve real-world problems across drug discovery, materials science, cybersecurity, and navigation. They build large quantitative models (LQMs) grounded in physics and chemistry to predict molecular properties, accelerate therapeutic design, and simulate complex chemical and catalytic processes, while also applying their expertise in AI and post-quantum cryptography to enhance digital security and navigation in GPS-denied environments.

📋 Description

• 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

• 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

• 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