Job description

Meta is seeking a senior technical leader in Data Infrastructure to help shape the systems that power our data storage, processing, and analytics across Meta's products. Our data-infrastructure teams build and operate foundational platforms at extreme scale, world's largest Datawarehouse/Lakehouse, distributed query engines, storage and table formats, and streaming/real-time processing systems that handle exabyte-scale data with high reliability and low latency. In this role you will set technical direction for large, cross-cutting data-infrastructure efforts, drive architecture for systems used by thousands of engineers across the org, and be the custodian of Meta's data systems in service of Frontier Models, Machine Learning Models, Analytics and Operational/Reliability Systems. We're looking for someone who has designed or led the creation of foundational data systems, query engines, storage/table formats, streaming frameworks, or a comparable large-scale data platform, and who can operate as a broad systems generalist grounded in deep data-infrastructure expertise. Define and drive the long-term technical vision and architecture for critical systems infrastructure spanning multiple engineering organizations, ensuring designs are reliable, scalable, and built to stand the test of time Identify and solve the hardest systems-level engineering problems across the stack, including those that cross abstraction boundaries, span multiple teams, or have resisted resolution by others Establish extensible architectural foundations, coding standards, and systems design principles that drive consistency and quality across organizations and platforms Lead the evolution of core technology stacks and paradigms, including planning and executing complex migration and renewal projects in mature, high-scale technical environments Define and operationalize performance targets, reliability guardrails, and quality standards at ecosystem scale, driving cross-organizational culture and process around systems health Develop invariants and systemic defenses that prevent entire categories of reliability, security, and correctness issues across the systems domain Collaborate with product, infrastructure, and cross-functional leaders to translate ambiguous business requirements into durable systems solutions, influencing technical direction across multiple organizations Mentor and develop engineers across the organization into cross-functional technical leaders who own outcomes end-to-end, and actively contribute to recruiting engineers with critical systems expertise Apply AI-native workflows and tooling as a force multiplier to accelerate systems design, debugging, and engineering efficiency at organizational scale Define new metrics and measurement frameworks for long-term, cross-team systems initiatives, connecting technical outcomes to organization-level priorities Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 12+ years of experience in systems software engineering, including design and implementation of large-scale distributed systems, operating systems, storage systems, or networking infrastructure Experience architecting and owning critical systems that operate at global scale, with demonstrated impact on reliability, performance, and engineering efficiency across multiple teams or organizations Experience leading multi-year technical strategy and roadmap development, including gaining cross-organizational alignment and driving execution through ambiguity Experience identifying and resolving systemic classes of bugs, performance regressions, or reliability issues that span multiple systems or abstraction layers Experience influencing technical direction and engineering culture across large organizations, including establishing standards and practices adopted broadly beyond a single team Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience rearchitecting or rebuilding foundational systems infrastructure from the ground up, including evaluating trade-offs between incremental improvement and greenfield approaches Track record of industry-level impact in systems engineering, such as open source contributions, published research, or widely adopted architectural patterns Experience driving technical and cultural change at the company level, including defining engineering excellence programs or cross-organizational reliability initiatives Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience applying AI and machine learning techniques to systems problems such as resource scheduling, anomaly detection, performance optimization, or automated incident response

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machine learningai

Platform: meta
Effort: medium

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