Senior Research Scientist, Battery Materials Simulation

ABOUT SANDBOXAQ SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors. We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders. At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact. THE OPPORTUNITY Introduction to the team: The Batteries vertical sits at the intersection of ChemSim and AI Sim, where SandboxAQ uses physics-based simulation, proprietary datasets, and Large Quantitative Models (LQMs) to discover and optimize next-generation battery materials. Our 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. Introduction to the role: We are seeking a highly skilled Senior Research Scientist in Battery Materials Simulation to join our growing team. The ideal candidate will have deep expertise in applying advanced simulation techniques, including Density Functional Theory (DFT), Molecular Dynamics (MD), and machine learning (ML), to battery materials discovery and optimization. This role will focus on developing computational workflows and AI-driven approaches to accelerate the design of next-generation battery materials, including cathodes, anodes, electrolytes, interfaces, and interphases. Experience modeling surface chemistry, interfacial degradation mechanisms, and electrochemical reaction pathways is highly desirable. As a senior member of the team, the candidate will provide technical leadership, mentor junior scientists, and drive the execution of strategic research programs in collaboration with internal and external partners. See how SandboxAQ is helping build America's semiconductor supply chain from the materials up https://www.sandboxaq.com/post/sandboxaq-helping-build-americas-semiconductor-supply-chain KEY RESPONSIBILITIES - Conduct advanced simulations using DFT, MD, enhanced sampling methods, and ML-based approaches for battery materials and electrochemical systems. - Model surface reactions, interfacial degradation mechanisms, and electrochemical processes, including cathode-electrolyte interfaces (CEI), solid-electrolyte interphases (SEI), solid-state electrolyte interfaces, and reaction pathways under operating conditions. - Develop and deploy computational workflows for high-throughput materials screening, reaction modeling, and materials optimization. - Lead high-fidelity data generation campaigns and develop ML force fields and surrogate models for battery materials and interfaces. - Employ data-driven approaches to analyze large computational and experimental datasets to uncover new insights into materials behavior. - Guide project scoping, execution, and delivery while working closely with cross-functional teams. - Provide technical direction for battery research roadmaps, translate high-level project goals into technical milestones, and mentor junior scientists in best practices for both ML and physics-based modeling. - Collaborate with internal teams, academic collaborators, government partners, and industrial customers to deliver impactful materials innovation. - Effectively communicate research findings through scientific publications, conference presentations, client-facing presentations, and technical reports. ESSENTIAL SKILLS & EXPERIENCES - Ph.D. in Materials Science, Chemical Engineering, Chemistry, Physics, Computer Science, or a related field. - 5+ years of industry experience in computational battery materials research beyond the Ph.D. - Strong theoretical foundation in thermodynamics, kinetics, electrochemistry, and materials science. - Proficiency in DFT and atomistic simulation tools (e.g., VASP, Quantum ESPRESSO, CP2K). - Familiarity with state-of-the-art machine learning force fields and frameworks (e.g. MACE, TensorNet, NequIP, Allegro, or FairChem). - Experience modeling surfaces, interfaces, reaction pathways, and electrochemical systems. - Experience training and evaluating ML models for materials property prediction and materials discovery. - Experience with