Senior Metrology Development Engineer

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We are seeking a Senior Metrology Development Engineer to develop data processing algorithms that integrate machine learning and physics simulations into our x-ray data processing pipelines, as well as build application engineer-focused data visualization and statistical analysis software. This role sits at the intersection of applications engineering and algorithm/software development, translating early-stage experimental concepts into robust, scalable data analysis solutions.

The ideal candidate combines strong physics intuition, statistical/data science expertise, and practical software development skills to translate emerging metrology applications into robust algorithms and user-facing tools.

Algorithm Development:

• Develop and prototype data analysis algorithms for X-ray metrology systems, including X-ray fluorescence (XRF) and X-ray diffraction (XRD).
• Build Python-based proof-of-concept (POC) algorithms for spectral fitting, peak analysis, and quantitative materials characterization.
• Collaborate with software engineers to productionize and integrate validated algorithms into scalable software pipelines.
• Develop statistical and physics-informed approaches for improving measurement robustness, accuracy, and repeatability.
• Develop robust methods for analyzing noisy, sparse, or high-dimensional experimental datasets, including uncertainty quantification and error propagation.

Software Development:

• Design and implement internal analysis tools that improve the efficiency of Applications Engineers.
• Develop Python-based utilities for data exploration, visualization, and statistical analysis of metrology datasets.
• Build lightweight tools, scripts, and dashboards that allow engineers to rapidly test new analysis approaches on experimental data.
• Contribute to version-controlled codebases and collaborate with the software team to ensure maintainable, scalable implementations.
• Write clean, modular, and well-documented code that can evolve from rapid prototypes into production-quality implementations.

Machine Learning & Advanced Data Methods:

• Explore and integrate machine learning and statistical modeling techniques for metrology data analysis.
• Develop hybrid physics + data-driven models that enhance the interpretation of complex measurement data.

Team Collaboration:

• Partner with Applications Engineers to understand emerging customer applications and measurement challenges.
• Work closely with physicists, materials scientists, and software engineers to bring new analysis methods from concept to deployment.
• Document algorithms, analysis methods, and tools to support long-term maintainability and knowledge transfer.
• Act as a technical bridge between applications engineering and software development, translating domain-specific problems into implementable algorithms and tools.

Education & Experience:

• Ph.D. or M.S. in Materials Science, Physics, Electrical Engineering, Data Science, or a related field.
• Experience working with semiconductor process characterization or materials metrology data.
• Demonstrated experience developing data analysis algorithms or scientific software for experimental datasets.
• Experience translating experimental measurements into quantitative models and analysis pipelines.

Technical Skills:

• Strong programming experience in Python for scientific computing and data analysis.
• Experience with scientific libraries such as NumPy, SciPy, Pandas, and visualization tools (Matplotlib, Plotly, etc.).
• Experience developing data analysis pipelines and reusable codebases, not just one-off scripts.
• Experience with curve fitting, optimization, or signal processing techniques.
• Experience with version control (Git) and collaborative software development workflows (e.g., code reviews, branching strategies)

Preferred Skills:

• Experience with X-ray metrology techniques (XRF, XRD, XRR, or related methods).
• Experience with machine learning frameworks.
• Experience building data visualization dashboards or analysis GUIs for scientific workflows.
• Experience developing tools that enable non-programmers to interact with complex datasets.
• Background in semiconductor process development or failure analysis.
• Japanese language proficiency (spoken and/or written) and experience collaborating with Japan-based engineering teams is strongly preferred.