Improved reliability and scalability of machine learning data infrastructure by designing and implementing new metadata management workflows, migrating data pipelines to use batch processing, introducing new integration testing frameworks, and implementing new APIs for flexible database querying.

Collaborated closely with operations specialists and ML researchers to implement new data annotation features for fine-grained object detection/tracking and human preference driving feedback.

Designed and implemented a new unclassified obstacle visualization within a driving simulator application, which led to a 20-50% decrease in predicted collisions with the obstacle type across all scene sets.

Optimized blob storage for petabytes of new ML training data, leading to an ~80% reduction in storage size and costs.