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Transitioned from Software Engineering to ML Engineering, specializing in high-severity, low-prevalence ad detection for DMAT (Don't Miss a Thing) system

Improved model action rate by 15% and doubled enforcement for critical harm categories including human exploitation, housing discrimination, and racial discrimination

Led cross-functional collaboration with human labelers and product managers to optimize datasets and model performance, directly managing annotation workflows and UI/UX improvements

Identified and resolved critical bug in Political Ads detection system that was inflating accuracy metrics; implemented corrected detection thresholds that maintained performance while improving real-world detection coverage in time for 2020 Election.

Partnered with executive stakeholders on policy development for nuanced harm detection scenarios and feature engineered embeddings for multi-model integration