Staff Research Scientist (Catalyst Simulation)

TL;DR

Staff Research Scientist (Catalyst Simulation) (AI Simulation): Lead microkinetic and reactor-level modeling that translates atomistic simulation and ML-potential energetics into process-scale predictions for industrial catalysis. Focus on designing end-to-end modeling workflows, validating against experimental data, and mentoring scientists while driving decision-ready results for industrial partners.

Company

hirify.global develops AI solutions, including Large Quantitative Models (LQMs), for high-impact industries.

What you will do

• Design, build, and own microkinetic and reactor-level modeling workflows to produce process-scale observables for catalytic and specialty chemical synthesis.
• Lead application engagements with industrial partners by translating partner problems into modeling targets and delivering credible, decision-ready results.
• Partner with internal dataset, computational chemistry, and machine learning teams to specify energetics, descriptors, and uncertainty estimates needed for microkinetic workflows.
• Validate models against experimental data and drive iterative refinement with uncertainty quantification.
• Mentor junior research and computational scientists and contribute to publications, partner deliverables, and the technical roadmap for microkinetic modeling.

Requirements

• US Persons only (Permanent Residents or Citizens) due to U.S. Government contractual requirements.
• PhD in Chemical Engineering, Chemistry, Materials Science, or related field with deep specialization in microkinetic modeling, surface reaction engineering, or multi-scale catalysis simulation.
• 6+ years post-PhD experience applying microkinetic models in industrial or applied R&D contexts, including coupling atomistic energetics to reactor-level predictions.
• Strong publication or patent record across DFT-derived energetics, microkinetic modeling, and reactor-scale integration.
• Proficiency in Python and modern scientific software practices in an HPC environment; comfort using ML-trained force fields and foundation models as inputs to kinetic workflows.
• Ability to lead external industrial engagements and communicate results to non-specialist audiences.

Nice to have

• Direct experience with semiconductor-relevant catalytic chemistries.
• Experience using operando or in-situ characterization data to calibrate microkinetic models.
• Familiarity with Bayesian optimization, active learning, or uncertainty quantification for catalyst discovery.
• Experience operating in a CHIPS Act or other federally funded R&D program.

Culture & Benefits

• Flexible hybrid work arrangements, generous PTO, and a culture that respects focus time and recovery.
• Comprehensive health, dental, and vision insurance; 401(k) with company match; generous parental leave.
• Direct exposure to CHIPS Act-funded programs, mentorship, and dedicated learning budgets.
• Competitive base salary plus equity and performance-based incentives.

Hiring process

• Interview process includes guidance for candidates on using AI tools in interviews.