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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.
Key Responsibilities:
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.
Qualifications:
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.