Senior Metrology Development Engineer

Rigaku•The Woodlands, TX

About The Position

Join Rigaku in shaping a better world through new perspectives! 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.

Requirements

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.
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)

Nice To Haves

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.

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.