Sr. Materials Expert AI Driven

Sr. Materials Expert AI Driven

How You'll Make an Impact

• Lead the development and execution of AI-driven materials discovery strategies by designing machine learning models, computational simulations, and digital materials twins to predict material properties and accelerate innovation cycles
• Develop and integrate advanced computational workflows, including AI/ML models, multi-scale simulations, density functional theory (DFT), molecular dynamics (MD), finite element modeling (FEM), and emerging quantum computing approaches to enable next-generation materials development
• Build and manage materials data ecosystems by establishing data pipelines, supporting FAIR data standards, curating materials databases, and integrating experimental and computational datasets to improve data-driven decision-making
• Conduct advanced materials research focused on structure-property-process relationships, including material selection, composites, polymers, alloys, metallurgy, characterization, testing, and failure analysis to support industrial applications
• Lead interdisciplinary R&D programs by managing technical roadmaps, project milestones, research reviews, external partnerships, and collaboration across global teams, scientific networks, and innovation communities
• Drive process innovation and knowledge development by mentoring scientists and engineers, establishing new research methodologies, communicating technical findings to leadership, and translating scientific results into scalable business solutions and intellectual property

What You Bring

• Ph.D. or Master's degree in Materials Science, Chemical Engineering, Physics, Computer Science, Engineering, or a related technical field with a focus on computational materials science or a comparable discipline
• 8+ years of experience leading research, engineering, or technology development projects in materials science, computational modeling, AI-driven innovation, or industrial R&D environments
• Deep expertise in computational materials science, materials informatics, machine learning, data science, and simulation methods, with demonstrated experience applying AI/ML techniques such as neural networks, Bayesian optimization, generative models, or graph neural networks (GNNs)
• Strong experience with materials modeling, experimental-computational integration, HPC environments, large-scale data processing, and advanced simulation techniques; knowledge of quantum computing concepts and algorithms for materials modeling preferred
• Proven ability to lead cross-functional and matrix teams, influence stakeholders, manage complex technical programs, and translate scientific research into practical industrial applications