Software Quality Assurance Engineer

ASML•San Jose, CA

1d•Hybrid

About The Position

ASML is seeking a highly talented individual to join its software quality assurance team in San Jose, CA. This role is responsible for validating complex software algorithms used in semiconductor manufacturing, ensuring correctness, robustness, and reliability. The ideal candidate will possess strong logical reasoning, data analysis, and scientific problem-solving skills, combined with software testing expertise. Backgrounds in physics, mathematics, computational science, electrical engineering, or research are encouraged. The position involves close collaboration with software developers, application engineers, and domain experts to understand algorithm behavior, design test scenarios, analyze results, and identify software or model issues.

Requirements

Advanced degree in Computer Science, Mathematics, Physics, Electrical Engineering, Computational Science, or a related engineering or scientific field.
Proficiency in software development with coding skills in C++, Python, or similar programming languages.
Strong analytical and logical reasoning skills.
Ability to understand complex systems, identify potential issues, and propose effective solutions.
Ability to analyze data, compare software results, identify patterns, and explain anomalies.
Deep understanding of software development principles, design, architecture, and engineering practices.
Knowledge of test automation frameworks and tools.
Experience with software testing techniques and methodologies.
Strong communication, collaboration, and teamwork skills.
Ability to work with developers and domain experts to understand complex algorithmic or scientific software behavior.

Nice To Haves

Research experience, or strong analytical project experience is preferred.
Experience with research, modeling, simulation, numerical methods, optimization, computational imaging, lithography, EDA, or semiconductor software.
Experience validating algorithmic software, scientific software, simulation software, or computational workflows.
Experience using Python for data analysis, scripting, automation, result comparison, or visualization.
Experience with C++ debugging, code review, or white-box testing.
Experience designing tests for edge cases, boundary conditions, numerical precision, or result consistency.
Prior QA or SQA experience is helpful, but not required if the candidate has strong analytical, coding, and problem-solving skills.

Responsibilities

Conduct tests before product launches to ensure software runs smoothly and meets client needs while being cost-effective.
Monitor all stages of software development to identify and resolve malfunctions to meet quality standards.
Review requirements, specifications, algorithm design documents, and technical design documents to provide timely and meaningful feedback.
Create detailed, comprehensive, and well-structured test plans and test cases for computational lithography software.
Design test cases that validate algorithm correctness, numerical behavior, edge conditions, and system-level behavior.
Analyze software outputs to determine whether results are expected, explainable, and consistent with product requirements.
Perform modular tests at code level before or after code merge.
Develop Python and/or C++ based tools to compare results, analyze trends, visualize differences, and improve test efficiency.
Design, develop, and execute automated tests for complex software workflows.
Develop test tools that enable SQA to analyze results and execute tests efficiently.
Identify, record, document thoroughly, and track software defects.
Perform thorough regression testing when bugs are resolved, with emphasis on result correctness and unintended algorithm behavior changes.
Work closely with developers, application engineers, and product teams to understand system requirements and expected software behavior.
Investigate failures by analyzing input data, output data, logs, algorithm behavior, and implementation details.
Contribute to testing methodology for complex computational software, including validation of boundary cases, numerical stability, and result consistency.
Stay up to date with relevant testing methods, data analysis approaches, and software validation strategies.