Claire Mcquin
Staff Software Engineer at Indeed.com
Tacoma, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolClaire Mcquin is a Staff Software Engineer with 12 years of experience building reliable, production-grade systems and improving operational excellence at scale. Based in Tacoma, she currently drives near-real-time clickstream analytics at Indeed using Apache Flink, and previously strengthened platform reliability through load testing, observability, and infrastructure-as-code. Her background spans bioimage analysis at the Broad Institute—where she contributed notable modules to the widely used CellProfiler project and created a 3D image analysis tutorial adopted by scikit-image—to developer tooling and automation at Chef. She combines deep engineering craft in Python and distributed systems with a pragmatic focus on automation, testing, and monitoring to reduce maintenance overhead and speed delivery. A mathematician by training with degrees in math and computer science from the University of Washington, she brings analytical rigor to both research-grade open-source projects and large-scale production platforms.
12 years of coding experience
7 years of employment as a software developer
Bachelor of Science (B.S.), Mathematics, Comprehensive, 3.75, Bachelor of Science (B.S.), Mathematics, Comprehensive, 3.75 at University of Washington
Github Skills (11)
cellprofiler10
image-segmentation10
scikit-image10
segmentation10
python10
image-processing10
numpy10
implement9
algorithm9
algorithms9
computer-vision8
Programming languages (11)
Github contributions (5)
CellProfiler/CellProfiler
Aug 2016 - Mar 2018
An open-source application for biological image analysis
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 releases, 539 commits, 691 PRs in 1 year 7 months
Contributions summary:Claire contributed significantly to the CellProfiler project by implementing and modifying various image processing modules using Python. They developed features such as the "Watershed" module for image segmentation, the "GammaCorrection" module for image adjustments, and the "BlobDetection" module for detecting features. Additionally, they modified existing modules and added code to improve the efficiency and functionality of existing modules, such as the "NoiseReduction" module for image denoising, and the "MeasureImageIntensity" module for image analysis.
image-analysispythonneuroimagingbiologicalmicroscopy
mcquin/notebooks
Oct 2017 - Dec 2020
Contributions:6 PRs, 14 pushes, 7 branches in 3 years 1 month
jupyter-notebooknotebookjupyterjupyterlab
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