
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.
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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.
• Conduct advanced simulations using DFT, MD, enhanced sampling methods, and ML-based approaches. • Model surface reactions, interfacial degradation mechanisms, and electrochemical processes. • Develop and deploy computational workflows for high-throughput materials screening, reaction modeling, and materials optimization. • Lead high-fidelity data generation campaigns.
• 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 machine learning force fields and frameworks (e.g. MACE, TensorNet, NequIP, Allegro, or FairChem). • Experience modeling surfaces, interfaces, reaction pathways, and electrochemical systems. • Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX). • Experience with cloud and high-performance computing environments. • Demonstrated ability to independently drive complex technical projects.
• Comprehensive health, dental, and vision insurance • 401(k) with company match • Generous parental leave • Flexible hybrid work arrangements • Generous PTO • Career development opportunities • Direct exposure to CHIPS Act-funded programs
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