• Lead and grow a team of ML engineers focused on production ML systems • Lead model improvements in response to production issues, product feedback, and new research or platform advancements • Lead production release processes for ML services, including release planning, CI/CD, staged rollouts, and rollback procedures • Build and operate observability and on-call practices for ML features, including monitoring, alerting, dashboards, incident response, and post-incident reviews • Develop and maintain scalable evaluation frameworks, datasets, and automated regression tests to prevent quality regressions • Lead reliability, performance, and cost improvements for inference and serving, including capacity planning and meeting SLAs (latency, throughput, availability) • Partner with researchers, product, and platform teams to define quality bars and production readiness, including Trusted AI requirements • Establish and evolve production standards and governance across ML features (testing, evaluation methodology, release gates, model versioning and lineage) • Partner with platform and product teams to integrate ML capabilities into products