At Nightfall AI, I improved sensitive-data detection recall by approximately 17% by evaluating transformer-based representations across structured and unstructured content. I also designed self-supervised learning experiments to improve generalization across unfamiliar content formats.
I built reproducible training workflows with PyTorch, Ray, and MLflow, reducing experiment turnaround from days to hours. On AWS GPU infrastructure, I optimized distributed training to reduce costs by approximately 24% without sacrificing model quality.
At Scale AI, I developed machine learning workflows for data labeling and model evaluation, and implemented distributed dataset processing that reduced preparation time by approximately 29%. Earlier, at Amazon, I developed backend services and event-processing workflows, including a pipeline improvement that reduced peak backlog clearance from hours to minutes.
