Define and own the technical architecture of critical machine learning systems, including model training pipelines, inference infrastructure, and feature engineering platforms, ensuring reliability and scalability across billions of users Identify and solve the most complex ML systems challenges across multiple product areas, including issues that span model quality, training efficiency, serving latency, and data integrity Develop and establish extensible ML frameworks, modeling standards, and engineering practices that drive consistency and velocity across multiple engineering organizations Lead cross-functional technical strategy for machine learning initiatives, aligning research, infrastructure, and product teams around multi-year roadmaps that balance short-term delivery with long-term architectural health Apply AI-native workflows and tooling as a force multiplier to accelerate model development cycles, automate evaluation pipelines, and expand the scope of what engineering teams can deliver Define new metrics and data-driven decision-making principles for long-term ML projects, connecting model performance signals to organization-level business outcomes Proactively identify systemic reliability, privacy, and integrity risks in ML systems and build robust technical safeguards, partnering with compliance and policy teams to ensure responsible AI deployment Mentor engineers across the organization on ML systems design, debugging complex model behavior, and building production-grade AI systems, establishing yourself as a sought-after technical coach and technical leader Drive performance improvements across large-scale ML systems by identifying bottlenecks that span training, data loading, model serving, and hardware utilization, and leading cross-org efforts to resolve them Influence the broader ML engineering community through technical publications, design frameworks, and cross-industry engagement that advances the field