Cindee Madison
Senior Machine Learning Engineer at Google
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolCindee Madison is a Senior Machine Learning Engineer and engineering leader in Seattle with two decades of experience turning messy scientific and business data into reliable, production-ready systems. She blends hands-on ML and data engineering with people and product leadership—currently holding dual engineering manager and senior ML roles at Google after leading ML efforts and serving as CTO in startups. Her background spans neuroimaging research at UC Berkeley/LBNL to building recommendation and graph-driven systems in industry, and she’s a long-time open-source contributor to flagship neuroimaging Python libraries (nipy, nipype, nibabel) where she implemented and hardened ECAT support and workflow interfaces. Comfortable at the intersection of research and production, she applies statistical rigor, test-driven development, and scalable ETL to solve noisy, high-stakes problems. Colleagues know her for clear communication, creative problem-solving, and a knack for finding signal in the noise across domains from brain imaging to personalization.
20 years of coding experience
15 years of employment as a software developer
University of Minnesota Twin CitiesBA, Philosophy-Biomedical Ethics, BA, Philosophy-Biomedical Ethics at Macalester College
Github Skills (21)
data-formats10
python10
spu10
command-line-interface10
workflow-engine10
spim10
spi10
data-format10
numpy10
fs10
nifti10
neuroimaging10
spmd10
data-analysis10
brain-imaging9
Programming languages (2)
Github contributions (5)
nipy/nipype
Jul 2009 - Mar 2012
Workflows and interfaces for neuroimaging packages
Role in this project:
Back-end Developer
Back-end DeveloperContributions:181 commits in 2 years 8 months
Contributions summary:Cindee's commits primarily involve adding and modifying files related to the core functionality of the `nipype` library, particularly focusing on interfacing with neuroimaging tools like FSL and SPM. The user added initial files for interfaces and pipelines, establishing a foundational structure for integrating these tools into a workflow engine. The contributions include defining input specifications, creating command-line interfaces, and implementing essential methods for running and managing commands within the framework.
workflowneuroimagingpythondata-scienceworkflow-engine
nipy/nipy
Apr 2006 - Jun 2009
Neuroimaging in Python FMRI analysis package
Role in this project:Back-end Developer
Back-end DeveloperContributions:45 commits in 3 years 2 months
Contributions summary:Cindee primarily contributed to the `nipy/nipy` repository by debugging, refactoring, and implementing features within the ECAT (Emission Computed Axial Tomography) format. They addressed issues in the ECAT data processing, including data type mappings, origin calculations, and subheader handling. Furthermore, they added unit tests and refactored code to improve the structure and functionality.
connectivityneuroimagingpythonfmrielectrophysiology
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