Machine Learning Scientist – Molecular Force Fields

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🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $134.4k - $252k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

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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 producing training data for PFAS chemical space • Own reproducible infrastructure for physics-based simulation data generation • Determine level of theory, basis set, convergence criteria, sampling strategy, and error budget • Build automated, restartable, provenance-tracked workflows across cloud and HPC • Curate and version internally generated and public molecular and materials datasets • Select architectures, train, validate, and deploy machine-learned interatomic potentials for PFAS substitution • Fine-tune universal or foundation potentials, or train models from scratch when 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 production prediction campaigns across candidate libraries • Deploy trained models, datasets, and reproducible training pipelines in SandboxAQ tooling • Collaborate with generative modeling, molecular property ML, simulation, and materials science teams • Report error bars, hold-out performance, and domain-of-validity limits for predicted properties • Escalate when models are asked questions beyond their capability

🎯 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 the hands of other people who rely on it • Hands-on experience generating first principles training data at throughput 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 such as MACE, NequIP, Allegro, TensorNet, DeePMD, ANI-class, or FairChem/UMA-family models • Strong programming skills in Python • Hands-on experience with modern ML frameworks such as PyTorch and JAX • Experience training at scale on GPU clusters • Practical experience coupling learned potentials into production 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 for reactive chemistry • Experience with system construction and equilibration, ensemble and thermostat/barostat choice • 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 • Candidates cannot participate in foreign talent programs sponsored by China, Russia, Iran, or North Korea due to federal grant requirements

🏖️ Benefits

• Competitive base salary commensurate with experience • Equity • Performance-based incentives • Comprehensive health, dental, and vision insurance • 401(k) with company match • Generous parental leave • Flexible hybrid work arrangements • Generous PTO • Direct exposure to CHIPS Act-funded programs • Mentorship • Dedicated learning budgets • Reasonable accommodations for individuals with disabilities in job application procedures