● Led end-to-end relevance improvements for the Facebook Search (MRS) retrieval generator.

● Shipped international language support and improved English retrieval performance, achieving a 6% CTR lift.

● Revamped the training pipeline into a 2-stage process: Stage 1 emphasized model regularization to learn robust

signals from moderate-quality data; Stage 2 leveraged higher-quality data and targeted augmentation (data

masking, QWERTY typo simulation) to improve semantic understanding of query–document pairs.

● Strengthened evaluation and testing infrastructure by reducing offline-to-online metric gaps and introducing tiered

online experimentation, significantly accelerating iteration speed.

● Upgraded TensorBoard's trace viewer tool with critical TPU host communication insights, enabling users to identify

and resolve training pipeline bottlenecks faster.

● Engineered a robust end-to-end integration testing service for the profiling workflow, reducing version-related

issues by 90% and ensuring seamless releases of open-source packages.

● Crafted comprehensive documentation for Cloud TPU inference workload profiling, resulting in a 30% reduction of

customer support tickets and faster adoption of best practices.