# Physics Informed Machine Learning Scientist at ASML

ASML is hiring a Physics Informed Machine Learning Scientist in San Diego, United States. It pays $135k-$203k a year and Level rates it Builds AI ●●●●; you can [apply on ASML](https://jobsbylevel.com/go/3ff1febb-32f1-4ca2-9dd2-20001fecab9d?utm_medium=ai-agent).

AI Level 4, AI centrality 85 out of 100. San Diego, CA, USA.

## Details

- Company: [ASML](https://jobsbylevel.com/companies/asml)
- AI level: AI Level 4 (score 85 out of 100)
- Location: San Diego, CA, USA
- Salary: $135k-$203k
- Posted: June 29, 2026
- Apply: https://jobsbylevel.com/go/3ff1febb-32f1-4ca2-9dd2-20001fecab9d?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=physics-informed-machine-learning-scientist-at-asml-3591b4

## Prepare for this job

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## Description

ASML US, LP brings together the most creative minds in science and technology to develop lithography machines that are key to producing faster, cheaper, more energy-efficient microchips. We design, develop, integrate, market and service these advanced machines, which enable our customers - the world’s leading chipmakers - to reduce the size and increase the functionality of their microchips, which in turn leads to smaller, more powerful consumer electronics. Our headquarters are in Veldhoven, the Netherlands, and we have 18 office locations around the United States including main offices in Wilton, CT, Chandler, AZ, San Jose, CA and San Diego, CA. Job Mission Join a pioneering research team developing next-generation lithography light source technologies. Our laser-produced plasma (LPP) system integrates high-power lasers, advanced optics, plasma-based EUV generation, sensing, and algorithm-driven control. The Physics Informed Machine Learning Scientist works on the Virtual Source team building integrated master-model frameworks to capture the tightly coupled, multi-physics behavior of the system—enabling system-level optimization, reducing uncertainty in future source configurations, and guiding early technology decisions. This role operates at the intersection of science, engineering, and modeling to define future source architectures. You will contribute by building data pipelines, developing data analysis and ML methodologies, integrating and advancing models, and defining validation experiments on test benches and research systems to anchor Virtual Source. Key Responsibilities: Establish a scalable data management framework spanning legacy and new datasets from test benches and source prototypes, ensuring data quality, accessibility, and structured readiness for seamless integration into ML workflows. Develop physics-informed machine learning models and scientific simulations to enable system-level tradeoff analysis and drive the definition and optimization of lithography source technology configurations. Adapt and integrate existing physics-based models into a master virtual model, and establish the necessary infrastructure for deployment and maintenance. Propose experimental anchoring studies, analyze test results, reduce model uncertainty through correlation building, and extract actionable knowledge from submodule- to full-system-level analysis. Provide input to technology roadmaps, identify de-risking activities and key scientific learning objectives, and contribute to experimental design to establish design guidelines, performance requirements, and procedures for product teams. Troubleshoot code and algorithms required for source operation, data streaming, storage, and queries. Document learnings and communicate knowledge to engineering and product development teams to guide product improvement and the release of new product nodes. Work independently and collaboratively to deliver on stated objectives, whether pursuing new knowledge, demonstrating new capabilities, or characterizing existing performance. Perform other duties as assigned or required. Qualifications Ph.D. with a minimum of 3+ years of experience or a Master’s degree with at least 6+ years of experience in an analytical field such as mathematics, physics, or engineering, with extensive experience in physics-informed machine learning and model integration into scalable master models. Experience solving complex, open-ended modeling problems using optimization and deep learning methodologies, with strong expertise in data management and building scalable data and training pipelines for end-to-end model development and training. Strong software development skills in Python, with experience in deep learning frameworks (e.g. PyTorch or JAX); proficiency in C/C++, and Matlab is a plus. Experience with database tools, automation frameworks, and experimental tracking platforms (e.g. MLflow) for managing end to end ML lifecycle. Experience working in cloud and

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