Machine Learning Scientist - Physics

ASML•San Diego, CA

1d•$135,375 - $203,063•Onsite

About The Position

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 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.

Requirements

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 development environments such as Azure Kubernetes Service (AKS), Google Distributed Cloud Edge (GDCE), Apache Spark, Azure Databricks, and related technologies is a plus.
Ability to clearly and logically communicate ideas and knowledge to various audiences.
Demonstrated ability to work effectively as a part of a team and lead investigation and research efforts involving multiple stakeholders and constraints.
Proven ability to build trust and credibility, enabling effective leadership through influence.
The successful candidate will not only have excelled in their technical field, but will have demonstrated inter-personal and communications strengths.
Deep understanding of scientific research methods and strong curiosity.

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.