Job Description:

• Develop large-scale datasets and machine-learned force fields or potentials for materials discovery.
• Deploy and support the design and implementation of computational workflows simulating the reactivity and dynamics of catalytic materials across atomic, mesoscale, and continuum scales.
• Collaborate with platform, machine learning, and software engineering teams to continuously improve the efficiency and performance of discovery workflows.
• Support client engagements focused on the research, development, discovery, and optimization of novel heterogeneous catalyst materials.
• Prepare and deliver high-impact reports, presentations, and publications to communicate research findings to internal, academic, and industry partners.
• Mentor junior scientists and interns in catalysis, reaction engineering, and computational methods, championing best research and software practices.

Requirements:

• PhD in Chemical Engineering, Materials Science, Chemistry, or an equivalent/related discipline, or equivalent practical experience.
• Experience developing and utilizing modern atomistic simulation methods, including machine-learned force fields and microkinetic models for heterogeneous catalysis.
• Experience performing in silico catalyst discovery and/or optimization projects.
• Strong background in software development with Python, particularly for computational materials analysis, and experience implementing open-source packages in an HPC environment.
• Ability to collaborate effectively within diverse, interdisciplinary teams.
• A strong publication record demonstrating interdisciplinary collaboration and the ability to adapt to new technologies and methodologies in catalysis research.

Benefits:

• Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions
• Retirement savings with company matching
• Paid parental leave
• Inclusive family-building benefits
• Fully remote
• Flexible paid time off
• Company-wide seasonal breaks
• Support for flexible work arrangements
• Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs