Job 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