Research Scientist, Battery Materials Simulation

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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

• 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

• 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

• 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.