Staff Research Scientist, Catalyst Simulation

🕒 June 3

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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

• Lead the translation of reaction energetics derived from atomistic simulations and machine learning potentials into process-scale predictions. • Own the design and execution of microkinetic and reactor-level modeling workflows for industrially relevant catalytic chemistries. • Connect atomistic simulation outputs to activity, selectivity, and catalyst lifetime predictions. • Partner with industrial validation partners to test and refine models against real-world data. • Mentor junior scientists and shape the technical roadmap for microkinetic capability within SandboxAQ.

🎯 Requirements

• PhD in Chemical Engineering, Chemistry, Materials Science, or a related field, with deep specialization in microkinetic modeling, surface reaction engineering, or multi-scale catalysis simulation. • 6+ years of post-PhD experience (or equivalent) building and applying microkinetic models in an industrial or applied R&D context, including coupling atomistic energetics to reactor-level predictions. • Strong publication or patent record demonstrating expertise across DFT-derived energetics, microkinetic modeling, and reactor-scale integration. • Proficient in Python and modern scientific software practices in an HPC environment; comfortable working with ML-trained force fields and foundation models as inputs to kinetic workflows. • Demonstrated ability to lead application-driven scientific engagements with external industrial partners and to communicate results to non-specialist audiences. • Must qualify as a U.S. Person (Permanent Resident or Citizen).

🏖️ 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 and dedicated learning budgets for continued growth