Research Scientist, Battery Materials Simulation

SandboxAQ

1d•$112,000 - $210,000•Hybrid

About The Position

SandboxAQ is seeking a highly skilled Research Scientist in Materials Simulation to join their dynamic team. The Batteries vertical at SandboxAQ uses physics-based simulation, proprietary datasets, and Large Quantitative Models (LQMs) to discover and optimize next-generation battery materials. The goal is to compress slow, empirical battery R&D into an AI-driven workflow spanning prediction, simulation, and materials discovery for high-impact applications including solid-state batteries, Cobalt-free cathodes, beyond Li-ion cell chemistries, and resilient energy storage systems. The ideal candidate will have a strong background in applying advanced simulation techniques, particularly Density Functional Theory (DFT) and Molecular Dynamics (MD), to battery materials. Additionally, expertise in building, fine-tuning or optimizing AI models for chemistry and material discovery is required.

Requirements

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.

Nice To Haves

Contributions to open-source projects related to MLIPs.
Authorship of publications in high impact peer-reviewed journals or conferences.
Demonstrated ability in project leadership within an industrial setting, including managing teams, timelines, and deliverables.

Responsibilities

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.