AI Engineer

ASML•San Diego, CA

10d•$129,188 - $189,475

About The Position

ASML develops advanced lithography systems for semiconductor manufacturing. Because these systems operate under demanding physical and chemical conditions, the company continually seeks materials with improved performance, reliability, and lifetime. The AI Engineer, Materials Discovery, develops and applies artificial intelligence, machine learning, and computational materials science methods to accelerate the discovery and optimization of materials for current and future ASML products. The role sits at the intersection of AI, atomistic simulation, materials science, and experimental validation, working with computational scientists, experimental researchers, and product engineering teams. The position involves developing high-throughput computational discovery and screening workflows, applying AI-enabled methods alongside established simulation approaches, and creating Python-based tools for materials data and modeling. The engineer evaluates predictions, collaborates on experimental validation, establishes computation–experiment feedback loops, and assesses emerging technologies for relevance to ASML engineering challenges. The role also translates promising materials concepts into product-relevant solutions and communicates methods, results, and recommendations to diverse stakeholders.

Requirements

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.
Strong foundation in computational materials science, computational chemistry, or computational physics.
Strong Python programming and scientific computing skills.
Working knowledge of modern machine learning frameworks and workflows, such as PyTorch or equivalent tools.
Ability to work with large computational datasets and develop reproducible analysis pipelines.
Understanding of materials synthesis and characterization methods and their relationship to computational predictions.
Ability to critically assess model accuracy, uncertainty, applicability, and limitations.
Strong analytical and problem-solving skills.
Ability to communicate complex computational and AI concepts to experimental scientists and engineering stakeholders.
Ability to collaborate effectively across computational, experimental, software, and product engineering teams.
Qualified candidates must be legally authorized to access controlled technology under the United States Export Administration Regulations before beginning work.

Nice To Haves

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.

Responsibilities

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 accelerate materials simulation and discovery.
Combine AI-enabled methods with established 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.
Evaluate computational predictions and prioritize promising materials and compositions for experimental investigation.
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 accelerate materials optimization.
Assess emerging AI and computational materials technologies and determine their applicability to ASML engineering challenges.
Work with multidisciplinary product and engineering teams to translate promising materials concepts into solutions relevant to ASML products.
Document methods, assumptions, results, and technical recommendations, and communicate them effectively to technical and non-technical stakeholders.