Design, develop, and deploy large-scale machine learning models and AI pipelines across Meta's product surfaces, including ranking, recommendation, and generative AI systems Own the end-to-end technical design of AI/ML systems, evaluating architectural trade-offs and selecting appropriate modeling approaches, training strategies, and inference frameworks Instrument and profile ML training and serving pipelines to identify performance bottlenecks, reduce latency, and optimize compute efficiency at scale Establish and maintain model evaluation frameworks, including offline metrics, online experimentation, and monitoring to ensure production model quality and reliability Collaborate with product managers, data scientists, and research scientists to translate product requirements into ML problem formulations and deliver measurable improvements to key metrics Contribute to the team's AI tooling and infrastructure by building reusable components, improving developer workflows, and driving adoption of best practices in model development and deployment Apply privacy-by-design principles and integrity safeguards throughout the ML development lifecycle, partnering with cross-functional teams to meet data governance and compliance requirements Mentor other engineers on ML system design, modeling techniques, and engineering craft, and actively contribute to recruiting and technical community programs Use AI-augmented development workflows to expand scope across data analysis, product thinking, and operational readiness beyond core modeling work Drive staged rollouts of new models using feature flagging and A/B testing frameworks, proactively monitoring for regressions and leading incident retrospectives to prevent recurrence