AI Engineer

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 responsibilitie s

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.

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.

Requirements

• 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.
• 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) paid holidays throughout the calendar year. For more information, please contact the Recruiter or click on this link Compensation & Benefits in the US .
• All new ASML jobs have a minimum application deadline of 10 days.
• This position requires access to controlled technology, as defined in the United States Export Administration Regulations (15 C.F.R. § 730, et seq.). Qualified candidates must be legally authorized to access such controlled technology prior to beginning work. Business demands may require ASML to proceed with candidates who are immediately eligible to access controlled technology.
• Inclusion and diversity
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• Please note: This email address is solely intended to provide a method for applicants to initiate ASML’s process to request accommodation(s). Any recruitment questions should be directed to the designated Talent Acquisition member for the position.