Software Engineer, AI Specialist - Monetization (Technical Leadership)

Responsibilities

• Identify and solve the most complex AI systems engineering challenges across the organization, including architecting large-scale machine learning training and inference infrastructure that operates at Meta's global scale
• Define extensible technical foundations and cross-organizational standards for AI model development, evaluation, and deployment pipelines that favor consistency and long-term maintainability
• Drive the technical vision and multi-year roadmap for AI platform capabilities, influencing priorities across multiple engineering teams and cross-functional partners including research, product, and data science
• Evaluate emerging AI architectures, model paradigms, and industry developments to identify opportunities and risks relevant to Meta's competitive position, and translate findings into actionable engineering strategy
• Lead the design and implementation of AI systems where correctness, reliability, and performance are rigorously proven, establishing invariants and testing frameworks that prevent entire categories of model and system failures
• Identify where AI tooling and automation can eliminate entire categories of engineering work, and drive adoption of AI-native workflows across engineering teams to exponentially increase organizational throughput
• Collaborate with research scientists and applied researchers to translate novel AI techniques from prototype into production systems that deliver measurable improvements to key product metrics
• Mentor engineers across the organization by providing customized technical coaching, leading engineering programs such as architecture reviews and AI craft initiatives, and establishing a culture of rigor and thoroughness
• Partner with legal, policy, and compliance teams to ensure AI systems meet privacy, security, and integrity standards, and set the bar for responsible AI development practices across the business domain
• Define new metrics and data-driven decision-making principles for long-term, cross-team AI initiatives, connecting technical outcomes to organization-level priorities and business impact

Minimum Qualifications