AI Research and Development Engineer

ASML US, LP. (US30); San Jose (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

ASML in San Jose, CA seeks AI Research and Development Engineers to lead end-to-end LLM/VLM development for semiconductor inspection. You will curate data, train or fine-tune models, evaluate performance, and optimize inference, deploying solutions that address real-world engineering challenges.

The role requires hands-on experience with modern DL frameworks (Python, PyTorch), transformer architectures, and distributed training, with a strong emphasis on rigorous evaluation, reproducibility, and

Qualifications

• Hands-on experience training or fine-tuning LLMs and/or VLMs end to end.
• Experience with Python and PyTorch.
• Strong understanding of transformer-based models and training dynamics.
• Experience with distributed multi-GPU environments and debugging training issues.
• Excellent analytical thinking and problem-solving.

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.
• Build and maintain high-quality training and evaluation datasets, including data cleaning, labeling strategies, synthetic-data generation, quality checks, and contamination controls.
• Establish robust evaluation and benchmarking frameworks covering model quality, task performance, robustness, hallucination, safety, latency, throughput, and cost.
• Develop Agentic AI solutions that integrate trained models with tools, planning, memory, and workflow orchestration.
• Apply advanced LLM/VLM techniques to semiconductor inspection and other complex engineering tasks.
• Collaborate with cross-functional teams and customers to identify research opportunities and validate technologies.
• Support the transition of validated AI research into reusable platforms and potential product applications.

Skills

Python

PyTorch

Transformer models

Distributed training

Education

Master's or PhD in CS/AI/ML/EE

Job 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.

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 se