Production Engineering Manager, Network Infrastructure

Meta's infrastructure reliability depends on high-performing network production engineering teams that keep our global systems running at scale. We are looking for a Production Engineering Manager to lead a team of network production engineers responsible for the reliability, scalability, and operational efficiency of Meta's network infrastructure. In this role, you will drive technical strategy, unblock your team, and partner closely with network engineering, software infrastructure, and site reliability teams to ensure our network systems meet the demands of billions of users worldwide.

Responsibilities

• Lead and develop a team of network production engineers working on reliability, scalability, and operational efficiency of Meta's network infrastructure
• Drive roadmap creation and technical strategy for network production engineering, aligning team priorities with broader infrastructure goals
• Contribute hands-on to technical work including reviewing system designs, debugging complex network incidents, and identifying automation opportunities to reduce operational toil
• Partner with network engineering, software infrastructure, and capacity planning teams to ensure smooth execution of cross-functional initiatives
• Proactively identify and resolve issues that impact team velocity, including on-call load, technical debt, and gaps in tooling or automation
• Set clear goals and expectations for team members, provide timely and actionable feedback, and manage performance effectively
• Develop team members through mentorship, targeted project assignments, and active engagement in their career growth
• Collaborate with recruiting to grow the team by sourcing, interviewing, and onboarding network production engineers with the right skills
• Communicate team goals, technical context, and progress clearly to cross-functional partners and leadership
• Foster a team culture of engineering craft, operational excellence, and accountability to compliance and ethical standards

Minimum qualifications

• 8+ years of experience managing production engineering, site reliability engineering, or network engineering teams
• 2+ years of experience managing a networking-related engineering team, including recruiting, performance management, and team development
• 2+ years of experience software engineering or scripting experience with languages such as Python, Perl, C++, or Go
• 4+ years of experience with networking protocols including TCP/IP, BGP, MPLS, ISIS, and related technologies
• 4+ years of experience with network device configuration and administration across multiple vendors (e.g., Cisco, Juniper)
• Experience with large-scale network infrastructure operations, including incident response, reliability engineering, and capacity management
• Experience identifying and implementing automation to improve operational efficiency and reduce toil in network or infrastructure environments
• Experience driving technical roadmaps and executing engineering strategy in a network or infrastructure domain Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Proven experience in designing, developing, and operating distributed systems at scale, with an in-depth understanding of the challenges and opportunities in this space
• Experience of software and network debugging, profiling, and instrumentation techniques to ensure optimal system performance
• Experience designing and maintaining automated testing infrastructure to ensure the quality and reliability of our systems
• Knowledge of IB/RDMA/RoCE networks, including RDMA congestion control mechanisms, AI training workloads and the demands they exert on networks
• 6+ years of experience building software solutions for managing network infrastructure, with a focus on scalability and reliability
• Track record of improving on-call health and engineering efficiency metrics across a production engineering or infrastructure team
• Familiarity with network protocols and distributed systems concepts relevant to large-scale production environments
• Experience with software-defined networking, network automation frameworks, or network observability tooling at scale
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience integrating AI or machine learning tools into engineering workflows to drive measurable efficiency or quality improvements
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Preferred qualifications

• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Proven experience in designing, developing, and operating distributed systems at scale, with an in-depth understanding of the challenges and opportunities in this space
• Experience of software and network debugging, profiling, and instrumentation techniques to ensure optimal system performance
• Experience designing and maintaining automated testing infrastructure to ensure the quality and reliability of our systems
• Knowledge of IB/RDMA/RoCE networks, including RDMA congestion control mechanisms, AI training workloads and the demands they exert on networks
• 6+ years of experience building software solutions for managing network infrastructure, with a focus on scalability and reliability
• Track record of improving on-call health and engineering efficiency metrics across a production engineering or infrastructure team
• Familiarity with network protocols and distributed systems concepts relevant to large-scale production environments
• Experience with software-defined networking, network automation frameworks, or network observability tooling at scale
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience integrating AI or machine learning tools into engineering workflows to drive measurable efficiency or quality improvements
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)