AI Research and Development Engineer

Position Overview

We are seeking talented and motivated AI Research and Development Engineers to join our team at ASML, working at the intersection of advanced artificial intelligence and semiconductor inspection technologies.

The primary requirement for this position is hands-on experience training or fine-tuning LLMs and/or VLMs across the end-to-end model development lifecycle, including data preparation, model adaptation, training, evaluation, performance optimization, and deployment or inference.

This research-focused role involves developing advanced LLM/VLM capabilities and translating research concepts into functional prototypes for real-world engineering applications. Experience developing Agentic AI systems is strongly preferred and will be considered an important differentiator. Semiconductor inspection or SEM experience is beneficial but not required, as domain knowledge can be developed through collaboration with internal experts.

Key Responsibilities

• Own end-to-end LLM/VLM development workflows, including data curation and preprocessing, model selection, training or fine-tuning, evaluation, inference optimization, deployment, and iterative improvement.
• Design and execute model adaptation strategies such as supervised fine-tuning, instruction tuning, parameter-efficient fine-tuning, preference optimization, and continued pretraining, as appropriate for the use case.
• Build and maintain high-quality training and evaluation datasets, including data cleaning, labeling strategies, synthetic-data generation, quality checks, and contamination controls.
• Establish rigorous evaluation and benchmarking frameworks covering model quality, task performance, robustness, hallucination, safety, latency, throughput, and cost.
• Develop innovative Agentic AI solutions that integrate trained or tuned models with tools, planning, memory, and workflow orchestration.
• Apply advanced LLM, VLM, multimodal AI, machine learning, and deep learning techniques to semiconductor inspection and other complex engineering applications.
• Collaborate with cross-functional teams and customers to identify research opportunities, translate engineering requirements into model-development objectives, and validate emerging technologies.
• Support the transition of validated AI research and prototypes into reusable platforms and potential product applications.

Required Qualifications

• Master’s or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related field; equivalent practical experience may also be considered.
• Demonstrated hands-on experience training or fine-tuning LLMs and/or VLMs end to end, with direct ownership of data preparation, training execution, evaluation, performance optimization, and deployment or inference.
  • Practical experience with modern deep-learning frameworks and model-development tooling, especially Python and PyTorch.
  • Strong understanding of transformer-based models, tokenization, optimization, training dynamics, evaluation methods, and common model failure modes.
• Experience working with distributed training or multi-GPU environments and diagnosing training issues such as instability, overfitting, data-quality problems, and performance regressions.
• Ability to design meaningful experiments, interpret results, and make evidence-based trade-offs across model architecture, training data, model quality, compute requirements, latency, and cost.
• Strong programming skills with the ability to independently design, implement, test, and evaluate AI algorithms and prototypes.
• Excellent analytical thinking, independent problem-solving, and technical reasoning skills.
• Strong teamwork, adaptability, and ability to thrive in a fast-paced research and development environment.
• Excellent communication and presentation skills, with the ability to clearly articulate complex technical concepts and research findings.

Candidates will be evaluated primarily on demonstrated end-to-end LLM/VLM training and tuning accomplishments, technical depth, and research capabilities rather than years of professional experience. Experience limited to prompt engineering, RAG implementation, or hosted-model API integration alone does not satisfy the primary model-training requirement.

Preferred Qualifications

• Strongly preferred: Experience developing Agentic AI systems, including tool-using agents, planning, memory, multi-agent systems, agent orchestration, and intelligent workflows.
• Experience with LLM/VLM adaptation and alignment techniques such as LoRA or QLoRA, supervised fine-tuning, preference optimization, reinforcement learning, or related methods.
• Experience with LLM/VLM data pipelines, synthetic-data generation, experiment tracking, model registries, distributed training, optimized inference, quantization, or model serving.
• Experience evaluating LLM/VLM systems for factuality, robustness, safety, latency, throughput, and cost in real application workflows.
• Additional plus: Knowledge of semiconductor manufacturing, voltage-contrast inspection, scanning electron microscopy, e-beam inspection, or defect analysis. Prior SEM experience is not required.

Why Join Us?

• Cutting-edge innovation: Train, tune, and evaluate next-generation LLM/VLM technologies for advanced engineering applications.
• Research with real-world impact: Develop model capabilities and Agentic AI solutions with opportunities to influence future products.
• End-to-end ownership: Work across data preparation, model training, evaluation, optimization, deployment, and application integration.
• Dynamic and collaborative environment: Enjoy the agility and innovative spirit of a startup-like team within a global technology leader.
• Professional growth: Work on challenging multidisciplinary problems and continuously expand your technical expertise.

Travel Requirements

• Business travel may be required as needed.

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

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.

Inclusion and diversity

ASML is an Equal Opportunity Employer that values and respects the importance of a diverse and inclusive workforce. It is the policy of the company to recruit, hire, train and promote persons in all job titles without regard to race, color, religion, sex, age, national origin, veteran status, disability, sexual orientation, or gender identity. We recognize that inclusion and diversity is a driving force in the success of our company.

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Request an Accommodation

ASML provides reasonable accommodations to applicants for ASML employment and ASML employees with disabilities.  An accommodation is a change in work rules, facilities, or conditions which enable an individual with a disability to apply for a job, perform the essential functions of a job, and/or enjoy equal access to the benefits and privileges of employment. If you are in need of an accommodation to complete an application, participate in an interview, or otherwise participate in the employee pre-selection process, please send an email to USHR_Accommodation@asml.com to initiate the company’s reasonable accommodation process.

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.