Chemicals Applications Lead

Originally posted 22 October 2026 by the employer.

About the role

As the Chemicals Applications Lead on the Industry Applications team, you will lead work across the chemicals, agrochemicals, and catalysis sectors to identify where fault-tolerant quantum computing can improve chemicals R&D. You will convert technical opportunities into customer programs, product requirements, and application roadmaps.

What you'll do

• Lead computational catalysis initiatives across chemicals and agrochemicals, identifying and prioritizing use cases based on industrial relevance, technical feasibility, potential quantum advantage, validation path, and customer value.
• Translate industrial R&D questions and customer needs into computational specifications, covering target systems, observables, accuracy requirements, baselines, validation data, and resource needs alongside algorithms, ML, and product teams.
• Diagnose sources of uncertainty in catalyst modeling across electronic structure, sampling, solvation, thermodynamics, kinetics, and transport to design workflows spanning classical pre- and post-processing, quantum algorithms, and ML techniques.
• Lead technical discovery and customer engagements with chemical producers, national laboratories, and academic partners, owning customer programs from use-case definition through scope, milestones, validation, and risks.
• Build business cases linking FTQC-derived accuracy improvements to R&D outcomes, providing market perspectives, market sizing, and revenue forecasts.
• Develop technical briefings, proposals, white papers, and customer-facing materials for scientific and executive audiences.

What you'll need

• Ph.D. in computational/theoretical chemistry, chemical engineering, physical chemistry, physics, or a related field with 3+ years of relevant experience in computational catalysis or industrial catalysis R&D.
• Deep expertise in at least one of homogeneous, heterogeneous, electro-, or photocatalysis, with working knowledge of reaction pathways, transition states, free energies, solvation, finite-temperature effects, and experimental validation.
• Understanding of electronic-structure methods and their limitations in catalysis, particularly for transition metals, open-shell, or multireference systems (bond breaking, charge transfer).
• Experience with molecular dynamics (MD) and, ideally, machine-learned interatomic potentials (MLIPs).
• Proven track record connecting computational results to scientific or industrial decisions.
• Ability to evaluate technical approaches and assess where quantum computing adds commercial and scientific value.

Nice to have

• Experience translating technical capabilities into customer programs, research partnerships, product roadmaps, or government-funded work.
• Familiarity with quantum algorithms or hybrid classical-quantum workflows.

Skills: computational catalysis, quantum computing, customer programs, use-case definition

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