Meta's Edge & Network Services team is responsible for the deployment and operational support of production networks spanning Edge, Backbone, Optical, and Datacenter infrastructure across Meta's global footprint. In this role, you will serve as a hands-on network engineer focused on site services and deployment, ensuring the reliability, scalability, and performance of production network infrastructure that underpins Meta's ability to connect billions of people worldwide. You will partner with cross-functional teams to execute network deployments, drive operational excellence, and contribute to the evolution of global network standards.
Responsibilities
Deploy, configure, and validate production network infrastructure including switching, routing, and optical transport equipment across data center and edge sites Perform day-to-day operational support of site network services, including troubleshooting routing and switching protocol issues across BGP, IS-IS, MPLS, and RSVP-TE environments Plan and execute scheduled maintenance activities including hardware upgrades, software migrations, and decommissioning of production network assets Maintain accurate as-built documentation, asset inventory, and configuration records for all deployed network and physical infrastructure Collaborate with network design and architecture teams to implement scalable deployment standards and contribute to the development of new site service solutions Support integration and field testing of new hardware and software platforms prior to production rollout Partner with vendors and facilities teams to manage structured cabling, fiber-optic infrastructure, and physical layer dependencies at production sites Participate in an on-call rotation to provide 24x7 support for production network infrastructure and respond to network incidents Identify opportunities to leverage automation and AI-assisted tooling to streamline deployment workflows, reduce manual effort, and improve operational consistency Contribute to team-level goals by sharing operational learnings, documenting best practices, and advising cross-functional partners on complex network matters
Qualifications
6+ years of experience deploying, configuring, and operating large-scale production networks Experience with physical network infrastructure including structured cabling, fiber-optic cabling, and data center facility operations Experience working in multi-vendor network environments with hands-on exposure to enterprise or hyperscale networking hardware Experience with network protocols including TCP/UDP, DHCP, and DNS, and hands-on experience with both IPv4 and IPv6 environments Experience troubleshooting and configuring routing and switching protocols including BGP, IS-IS, MPLS, RSVP-TE, and VRRP Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated use of AI tools to redesign or optimize network operations workflows, with measurable outcomes such as reduced deployment time or improved documentation accuracy Experience with scripting and network automation using Python, Bash, or similar languages to improve deployment and operational workflows Experience with 40G/100G Ethernet technologies and optical transport technologies including CWDM and DWDM Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience with field-based network operations in data centers, colocation facilities, internet exchanges, or central offices Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies