AI Research and Development Engineer

Company: ASML

Location: San Jose, CA, USA

Salary: $162.8k - $238.7k per year

Type: Full-time

Posted: 2026-10-27

About this role

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 ...

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