Network Production Engineer

INE is a design team with full product lifecycle ownership. You will drive network products from requirements and RFC through design specification, proof-of-concept lab validation, and production operational accountability. Our portfolio spans more device roles and vendors than almost any other networking team at Meta - backbone, server, VPN, TOR, aggregation, MACsec, and more. If you want to solve scaling problems across a broad technology landscape rather than specializing in a single protocol domain, this is the role.

As a Production Engineer in the network organization at Meta, you will combine the skills of a software and network engineer. You will use data analysis to influence design decisions and leverage automation to achieve the scale and velocity that Meta's AI-era data center growth demands.

Responsibilities

Design, deploy, and maintain multi-vendor, multi-protocol infrastructure networks (OOB, PON, FacNet, DR) across every Meta datacenter and POP location Own the full product lifecycle - from requirements gathering and RFC through design specification, POC lab validation, configuration artifacts, and production operational accountability Write and review code, develop automation for the network deployment, configuration management, and release engineering workflow processes Drive New Platform Introduction (NPI) from concept through lab validation to production handoff, collaborating with production engineering teams for productionization and delivery teams for delivery at scale Develop audits, health checks, and monitoring to ensure network health and safe change management across the fleet Maintain technical relationships with networking vendors (Juniper, Cisco, Arista, Ciena), drive roadmap alignment, and lead hardware evaluations Define SLAs, implement metric collection and reporting for the network products you own Proactively identify cross-team gaps across the product portfolio and drive resolution through cross-functional collaboration Together with the rest of your team, share an oncall rotation and serve as an escalation contact for service incidents

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 6 years of work experience in network engineering, with emphasis on network design or architecture (not purely operations) 4+ years of work experience in coding in higher-level languages (e.g., Python, Go, C++), applied to network automation or infrastructure tooling Experience in developing and understanding network device configuration for multiple vendors (Juniper, Cisco, Arista, Ciena, or an equivalent) Protocol experience in one or more of BGP, OSPF, or ISIS - understanding of typical configurations and performance optimizations Experience developing software using AI models or integrating AI tools into engineering workflows Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience Experience with hardware evaluation, vendor management, and influencing network product roadmaps through cross-functional collaboration Experience in configuration and maintenance of network management systems or applications such as authentication servers (RADIUS/TACACS), logging infrastructure, or collection systems (SNMP/NETCONF/gNMI) Experience with IPSec for site-to-site or remote access VPNs Experience with XGS-PON or passive optical network technologies. Experience with out-of-band management networks, disaster recovery network infrastructure, or facility networking 2+ years of experience in network design or architecture, with product ownership from concept through production Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Expert knowledge of TCP/IP and IPv6 Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)