AI Engineer
- Compensation
- Not disclosed
- Location
- San Diego, CA, USA
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
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 approach
- 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.
Full description
Introduction to the job
ASML develops advanced lithography systems that enable semiconductor manufacturing. Our systems operate under demanding physical and chemical conditions, creating a continuous need for innovative materials with improved performance, reliability, and lifetime.
As an AI Engineer for materials discovery, you will develop and apply artificial intelligence, machine learning, and computational materials science methods to accelerate the discovery and optimization of materials for current and future ASML products. You will work at the intersection of AI, atomistic simulation, materials science, and experimental validation, collaborating with computational scientists, experimental researchers, and product engineering teams.
Role and responsibilities
As an AI Engineer, Materials Discovery, you will:
- Develop and execute high-throughput computational materials discovery and screening workflows.
- Apply emerging 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 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.
- 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 both technical and non-technical stakeholders.
**Education and experience
- 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.