AI Research and Development Engineer

ASML•San Jose, CA

1d•$162,750 - $238,700

About The Position

ASML is seeking motivated AI Research and Development Engineers to work at the intersection of artificial intelligence and semiconductor inspection technologies. The role focuses on hands-on, end-to-end training or fine-tuning of large language models (LLMs) and/or vision-language models (VLMs), including data preparation, model adaptation, training, evaluation, performance optimization, and deployment or inference. This research-focused position develops advanced LLM/VLM capabilities and turns research concepts into functional prototypes for real-world engineering applications. Developing Agentic AI systems is strongly preferred. Semiconductor inspection or scanning electron microscopy (SEM) experience is beneficial but not required; domain knowledge can be developed in collaboration with internal experts. The engineer will work with cross-functional teams and customers to identify research opportunities, translate engineering requirements into model-development objectives, validate emerging technologies, and support the transition of validated research and prototypes into reusable platforms and potential products. Business travel may be required as needed.

Requirements

Master’s or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related field; equivalent practical experience may 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 with distributed training or multi-GPU environments and diagnosing 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 and 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 work in a fast-paced research and development environment.
Excellent communication and presentation skills, including the ability to explain complex technical concepts and research findings.
Candidates are evaluated primarily on demonstrated end-to-end LLM/VLM training and tuning accomplishments, technical depth, and research capabilities rather than years of professional experience. Prompt engineering, RAG implementation, or hosted-model API integration alone does not meet the primary model-training requirement.

Nice To Haves

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.
Knowledge of semiconductor manufacturing, voltage-contrast inspection, scanning electron microscopy, e-beam inspection, or defect analysis. Prior SEM experience is not required.

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, including supervised fine-tuning, instruction tuning, parameter-efficient fine-tuning, preference optimization, and continued pretraining as appropriate.
Build and maintain training and evaluation datasets through data cleaning, labeling strategies, synthetic-data generation, quality checks, and contamination controls.
Establish evaluation and benchmarking frameworks covering model quality, task performance, robustness, hallucination, safety, latency, throughput, and cost.
Develop Agentic AI solutions that integrate models with tools, planning, memory, and workflow orchestration.
Apply 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.
Design, implement, test, and evaluate AI algorithms and prototypes, and communicate technical concepts and research findings.