# AI Research and Development Engineer at ASML

ASML is hiring an AI Research and Development Engineer in San Jose, United States. It pays $163k-$239k a year and Level rates it Builds AI ●●●●; you can [apply on ASML](https://jobsbylevel.com/go/cbfd9422-d9b1-4624-9b76-7951ffeaf7fe?utm_medium=ai-agent).

AI Level 4, AI centrality 99 out of 100. San Jose, CA, USA.

## Details

- Company: [ASML](https://jobsbylevel.com/companies/asml)
- AI level: AI Level 4 (score 99 out of 100)
- Location: San Jose, CA, USA
- Salary: $163k-$239k
- Posted: October 27, 2026
- Apply: https://jobsbylevel.com/go/cbfd9422-d9b1-4624-9b76-7951ffeaf7fe?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=ai-research-and-development-engineer-at-asml-d99510

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/ai-research-and-development-engineer-at-asml-d99510

## Description

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

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