AI Engineer - San Diego, CA, United States

Originally posted 29 October 2026 by the employer.

About the role

As an AI Engineer for materials discovery, you will develop and apply artificial intelligence, machine learning, and computational materials science methods to discover and optimize materials for ASML lithography systems.

What you'll do

- Develop and execute high-throughput computational materials discovery and screening workflows. - Apply AI and machine learning methods, including machine-learned interatomic potentials and surrogate models, to materials simulation. - Combine AI-enabled methods with computational approaches such as density functional theory (DFT), molecular simulation, and materials informatics. - Develop Python-based workflows for materials data generation, processing, modeling, analysis, and visualization. - Collaborate with experimental researchers to design validation strategies and interpret materials synthesis and characterization results. - Establish feedback loops between computation, machine learning, and experiment to improve predictive capability and materials optimization.

What you'll need

- PhD in materials science, chemistry, physics, chemical engineering, computational science, or a related technical discipline. - Experience applying computational methods to materials or chemical systems. - Experience developing scientific software or data-analysis workflows using Python. - Experience with machine learning, data-driven modeling, or AI methods applied to scientific or engineering problems.

Nice to have

- Experience with density functional theory, atomistic simulation, molecular dynamics, or related computational materials methods. - Experience with machine-learned interatomic potentials, graph neural networks, foundation models for materials, active learning, Bayesian optimization, or other AI-enabled materials discovery approaches. - Experience developing automated or high-throughput simulation and data pipelines. - Familiarity with materials databases and atomistic modeling tools such as ASE, pymatgen, VASP, Quantum ESPRESSO, LAMMPS, or comparable frameworks. - Experience connecting computational predictions with experimental materials synthesis or characterization. - Experience working in multidisciplinary R&D environments and translating research methods into engineering applications.

Skills: Machine Learning, Density Functional Theory, Python, Molecular Dynamics, PyTorch, Materials Discovery