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SandboxAQ

Research Scientist, Battery Materials Simulation

SandboxAQ

Research Scientist in Materials Simulation at SandboxAQ applying DFT and MD techniques. Focused on optimizing next-generation battery materials and collaborating with multi-disciplinary teams.

Posted 6/26/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $112,000 - $210,000 per yearWebsite

About the role

Key responsibilities & impact
  • Conduct advanced simulations using DFT and MD for solid-state materials, with a focus on predicting properties for solid-state electrolytes and interfacial degradation reactions.
  • Employ data-driven approaches to analyze large datasets derived from computational simulations and experiments to uncover new insights into materials behavior.
  • Conduct high-fidelity data generation campaigns and develop ML force fields for solid-state materials.
  • Guide and scope projects with clear deliverables alongside agile teams.
  • Collaborate closely with multi-disciplinary teams to independently prototype and scale cutting-edge, impactful materials design solutions.
  • Generate and evaluate hypotheses to assist design decisions and influence project direction by developing and deploying computational methods and workflows.
  • Effectively present and communicate research findings through scientific talks, blog posts, client-oriented presentations, and peer-reviewed publications.

Requirements

What you’ll need
  • Ph.D. in Materials Science, Chemical Engineering, Chemistry, Computer Science, or a related field is preferred.
  • 3+ years of hands-on experience in modeling complex solid-state battery materials, such as cathodes, anodes, solid-state electrolytes, and/or interfacial reactions at non-equilibrium states is highly desirable.
  • Proficiency in common DFT and MD simulation software (e.g., VASP, Quantum ESPRESSO, LAMMPS, ASE).
  • Experience with developing or using AI models for chemistry and material discovery using popular deep learning frameworks on CPUs and GPUs.
  • Proven ability to benchmark and compare domain specific AI models for materials discovery.

Benefits

Comp & perks
  • Comprehensive health, dental, and vision insurance;
  • 401(k) with company match;
  • Generous parental leave;
  • Flexible hybrid work arrangements;
  • Generous PTO;
  • Culture that respects focus time and recovery;
  • Direct exposure to CHIPS Act-funded programs;
  • Mentorship;
  • Dedicated learning budgets to support continued growth.

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Hard Skills & Tools
DFTMDdata-driven approachesML force fieldsmodeling complex solid-state battery materialsbenchmarking AI modelshypothesis generationcomputational methodsworkflow development
Soft Skills
project scopingcollaborationcommunicationpresentation skillsindependent prototyping
Certifications
Ph.D. in Materials SciencePh.D. in Chemical EngineeringPh.D. in ChemistryPh.D. in Computer Science