Meta
Production Engineering Manager
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Meta is seeking a Production Engineering Manager
Meta is seeking a Production Engineering Manager to lead a team responsible for the reliability, scalability, and operational excellence of Meta's production infrastructure and services. In this role, you will manage a team of production engineers who own the full lifecycle of systems — from capacity planning and performance optimization to incident response and automation. You will drive technical strategy, champion AI-augmented workflows, and partner closely with software engineering, infrastructure, and product teams to ensure Meta's services operate at global scale with high availability and efficiency.
Responsibilities
- Manage a team of production engineers delivering on reliability, scalability, and operational efficiency across multiple interdependent production systems
- Drive roadmap creation for infrastructure reliability initiatives, capacity planning, and automation efforts, increasing team scope as AI-driven productivity improves throughput
- Lead adoption of AI-augmented engineering workflows across the team, sharing learnings and best practices with the broader production engineering organization
- Contribute hands-on to technical work including code, system design reviews, and incident response, using AI tooling to expand personal and team reach across disciplines
- Partner cross-functionally with software engineering, data science, and product teams to unblock dependencies and ensure smooth execution of infrastructure and reliability projects
- Proactively identify and resolve sources of operational toil — including on-call load, alerting gaps, and technical debt — and implement automation to increase team efficiency and scope
- Set clear goals and expectations for individual team members, provide timely and actionable feedback, and actively develop engineers' skills including proficiency with AI-augmented workflows
- Establish and monitor service-level objectives, reliability metrics, and engineering efficiency indicators to maintain high engineering craft and product quality
- Communicate production system health, incident learnings, and infrastructure strategy effectively to engineering leadership and cross-functional stakeholders
- Recruit, onboard, and retain production engineering talent, ensuring the team structure minimizes single points of failure and supports sustainable growth
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Minimum Qualifications
- 4+ years of experience in production engineering, site reliability engineering, or systems software engineering
- 2+ years of experience managing production engineering or infrastructure engineering teams
- Experience driving reliability and scalability improvements for large-scale distributed systems, including incident management, capacity planning, and performance optimization
- Experience coding and debugging in at least one systems or scripting language (such as Python, C++, Go, or Bash) and contributing technically alongside a team
- Experience setting team goals, managing execution against roadmaps, and communicating infrastructure strategy to technical and non-technical stakeholders
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Preferred Qualifications
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Track record of cross-functional collaboration with software engineering and data science teams to co-own reliability outcomes for consumer-facing or infrastructure services
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience leading adoption of AI-assisted tooling or automation frameworks within an engineering team to expand operational scope and reduce toil
- Experience managing on-call rotations, defining service-level objectives, and implementing observability and alerting improvements at scale
- Familiarity with container orchestration, service mesh architectures, or large-scale deployment pipelines in a production environment
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
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