Meta is seeking a distinguished Software Engineer with deep AI specialization to drive transformative technical initiatives across Meta's AI-powered products and platforms. In this role, you will define and lead the architectural direction of large-scale AI systems — spanning foundation models, intelligent ranking and recommendation infrastructure, and applied machine learning pipelines — that serve billions of users across Meta's family of apps. You will identify and solve the hardest AI engineering challenges in the organization, set technical standards that span multiple teams, and leverage AI as a force multiplier to unlock capabilities previously considered intractable. This is a role for a technical leader who operates at the intersection of cutting-edge AI research and production-scale engineering, shaping both the systems and the culture that power Meta's AI future.
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
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 12+ years of experience in software engineering with a focus on AI, machine learning systems, or applied deep learning in production environments Experience architecting and delivering large-scale AI or machine learning systems — including training infrastructure, model serving, ranking, recommendation, or foundation model pipelines — that operate at significant scale Experience leading multi-team technical initiatives end-to-end, including defining strategy, driving cross-functional alignment, and delivering measurable outcomes against organization-level goals Experience identifying and resolving systemic engineering issues that span multiple systems or abstraction layers, including developing frameworks that prevent recurring classes of failures Experience communicating complex AI system designs and technical trade-offs in writing and presentations to both technical and non-technical audiences, including engineering leadership Experience applying AI and automation tooling to eliminate categories of engineering toil and measurably improve team-level or organization-level engineering efficiency Contributions to peer-reviewed AI or systems research (e.g., NeurIPS, ICML, ICLR, MLSys, OSDI) or demonstrated track record of translating research advances into production AI systems Experience with large-scale model training optimization, distributed training frameworks, or inference efficiency techniques such as quantization, distillation, or speculative decoding Experience defining and operationalizing privacy-preserving or safety-aware AI system designs in collaboration with policy, legal, or compliance stakeholders