AI Engineer

ASML US, LP. (US30); San Diego (CA); Sur place; USD 129 000 - 189 000; Plein temps

Avantages offerts par ce poste

Medical, dental, and vision insurance

401k plan

Vacation leave

Résumé du poste

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.

Responsabilités

• 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.

Connaissances

Python

Computational materials science

Machine learning

Data analysis

Scientific computing

Communication

Formation

PhD in materials science or related

Outils

ASE

pymatgen

VASP

Quantum ESPRESSO

LAMMPS

Description du poste

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

• 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.

Preferred qualifications

• 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

• 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.

The current base annual salary range for this role is currently:

$129,188-189,475

Pay scales are determined by role, level, location and alignment with market data. Individual pay is determined through interviews and an assessment of several factors that that are unique to each candidate, including but not limited to job-related skills, relevant education and experience, certifications, abilities of the candidate and pay relative to other team members.

Benefits

The Company offers employees and their families, medical, dental, vision, and basic life insurance. Employees are able to participate in the Company's 401k plan. Employees will also receive eight (8) hours of vacation leave every month and (13)