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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
- 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
Preferred Qualifications
- 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
$219,000/year to $301,000/year + bonus + equity + benefits
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