• Define the companys ML strategy: where ML should be applied across products, what infrastructure is required, and how to approach build vs. buy decisions. • Design and build production ML systems end-to-end — including data pipelines, model training workflows, evaluation frameworks, and inference serving. • Establish rigorous evaluation methodology to measure model quality, detect regressions, and support data-driven iteration. • Own the data strategy: determine what data is needed, how it should be labeled, how feedback loops are structured, and how models continuously improve. • Partner closely with product and backend engineers to integrate ML into customer-facing systems. • Write production-quality code within the existing codebase and contribute to architectural decisions. • Over time, help recruit, mentor, and lead the ML team as the function expands.