• Architect and significantly influence ML training/serving systems and tooling that unlock faster iteration and larger models. • Drive adoption and reliability of ML systems and own a portfolio of initiatives across multiple teams. • Improve efficiency: GPU utilization, training runtime, data loading, feature performance, and serving latency. • Write high-quality design docs; run design reviews; set standards for correctness, reliability, and velocity. • Partner cross-org on shared infra sequencing, requirements, and adoption. • Mentor other engineers and contribute to engineering best practices across the org.