About SandboxAQ

SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.

We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.

At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.

The Opportunity

Introduction to the team: The Catalysis team develops and validates new catalyst technologies for complex industrial applications, with a focus on high-purity chemical production, emissions treatment, and advanced sensing. The team brings together computational modeling, machine learning, experimental validation, and cross-functional collaboration to accelerate how promising catalyst concepts move from discovery to real-world use.

Introduction to the role: The Catalysis team is looking for a Staff Research Scientist, Catalyst Simulation to lead the translation of reaction energetics derived from atomistic simulations and machine learning potentials into process-scale predictions that advanced manufacturing facilities, OEMs, and chemical manufacturers can act on. This person will: (1) own the design and execution of microkinetic and reactor-level modeling workflows for industrially relevant catalytic chemistries, (2) connect atomistic simulation outputs to activity, selectivity, and catalyst lifetime predictions, (3) partner with industrial validation partners to test and refine these models against real-world data, and (4) mentor junior scientists and shape the technical roadmap for microkinetic capability within SandboxAQ.

Key Responsibilities

• Design, build, and own microkinetic and reactor-level modeling workflows that translate energetics derived from atomistic simulations and machine learning potentials into process-scale observables for complex catalytic and specialty chemical synthesis processes.

• Lead application engagements with industrial partners (e.g., specialty chemical manufacturers, equipment providers, and process technology developers), translating partner problems into well-posed modeling targets and delivering credible, decision-ready results.

• Partner closely with internal dataset, computational chemistry, and machine learning teams to specify the energetics, descriptors, and uncertainty estimates microkinetic workflows require, closing the loop between reaction-scale and process-scale modeling.

• Validate models against experimental data from scientific and academic partners; drive iterative model refinement and uncertainty quantification.

• Mentor junior research and computational scientists; contribute to publications, partner deliverables, and the technical roadmap for microkinetic modeling capabilities within SandboxAQ.

Essential Skills & Experience

• 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.

• Because this position supports specific U.S. Government contractual requirements, we can only consider US Persons (Permanent Residents or Citizens) at this time.

Highly Desired Skills & Experience

• Direct experience with semiconductor-relevant catalytic chemistries.

• Experience with operando or in-situ characterization data and using it to calibrate microkinetic models.

• Familiarity with Bayesian optimization, active learning, or uncertainty quantification applied to catalyst discovery.

• Experience operating in a CHIPS Act or other federally funded R&D program.

Why Join Us?

We offer competitive compensation, a comprehensive benefits package, and opportunities for professional growth.

• Compensation: Competitive base salary commensurate with experience, plus equity and performance-based incentives.

• Benefits: Comprehensive health, dental, and vision insurance; 401(k) with company match; generous parental leave.

• Work-Life Balance: Flexible hybrid work arrangements, generous PTO, and a culture that respects focus time and recovery.

• Career Development: Direct exposure to CHIPS Act-funded programs, senior scientific and executive leadership, mentorship, and dedicated learning budgets to support continued growth.

SandboxAQ Welcomes All

We are committed to fostering a culture of belonging and respect, where diverse perspectives are actively sought and valued. Our multidisciplinary environment provides ample opportunity for continuous growth - working alongside humble, empowered, and ambitious colleagues ready to tackle epic challenges.

Equal Employment Opportunity: All qualified applicants will receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.