Nancy Yuen is a senior finance and compliance leader with eight years in data-driven finance leadership and over two decades of experience across accounting, internal audit, SOX, and regulatory reporting. As Senior Director at SoFi and former Sr Manager of Revenue Accounting and SOX at Google, she blends technical rigor with program-level governance to scale controls and reporting in complex, high-growth environments. She teaches advanced and forensic accounting subjects at Saint Mary’s College, demonstrating a commitment to translating practitioner experience into academic instruction. Her background includes hands-on consulting and Big Four experience driving SOX, risk, and process improvement engagements for large financial and consumer clients. Beyond finance, she contributes to open-source infrastructure work—maintaining CI/CD runner images for the IREE ML compiler project—showing an uncommon cross-disciplinary fluency in both controls and DevOps. This mix of practical audit chops, teaching, and infrastructure contribution enables her to bridge finance, data, and engineering stakeholders effectively.
9 years of coding experience
Bachelor of Science Neurobiology Physiology and Behavior (NPB), Bachelor of Science Neurobiology Physiology and Behavior (NPB) at University of California, Davis
Master of Science in Accounting with Honors, Master of Science in Accounting with Honors at Saint Mary's College of California
Classical Piano and Violin, Classical Piano and Violin at Longy School of Music
A retargetable MLIR-based machine learning compiler and runtime toolkit.
Role in this project:
DevOps Engineer
Contributions:12 reviews, 27 PRs, 10 pushes in 6 months
Contributions summary:Nancy primarily focused on maintaining and updating the infrastructure for the IREE project, specifically related to the GitHub Actions runners. They made frequent updates to the runner image versions, including CPU, GPU, and ARM64 images, and deprecated specific machine types. These updates ensured the CI/CD pipeline and automated testing environments were up-to-date, optimized, and properly configured. They also addressed image-related issues to improve the stability of the build process.
A retargetable MLIR-based machine learning compiler and runtime toolkit.
Contributions:22 pushes, 19 branches in 3 months
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