AI Materials Discovery Engineer

ASML US, LP. (US30); San Diego (CA); On-site; USD 129,000 - 189,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401k plan

Vacation leave

Job summary

ASML is seeking an AI Engineer for materials discovery to advance AI, atomistic simulation, and experimental validation for materials used in ASML products. You will work at the intersection of AI, computation, and materials science with cross-disciplinary teams.

You will develop workflows, apply ML and surrogate models, evaluate predictions, and help translate promising materials concepts into ASML product solutions.

Qualifications

• PhD in materials science, chemistry, physics, chemical engineering, computational science, or 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.

Responsibilities

• Develop and execute high-throughput computational materials discovery and screening workflows.
• Apply emerging AI and machine learning methods to accelerate materials simulation and discovery.
• Combine AI-enabled methods with DFT, molecular simulation, and materials informatics.
• Develop Python-based workflows for materials data generation, processing, modeling, analysis, and visualization.

Skills

Python

Computational materials science

Machine learning

Data analysis

Scientific computing

Communication

Education

PhD in materials science or related

Tools

ASE

pymatgen

VASP

Quantum ESPRESSO

LAMMPS

Job description

ASML is seeking an AI Engineer for materials discovery to advance AI, atomistic simulation, and experimental validation for materials used in ASML products. You will work at the intersection of AI, computation, and materials science with cross-disciplinary teams.

You will develop workflows, apply ML and surrogate models, evaluate predictions, and help translate promising materials concepts into ASML product solutions.