Network Engineer, Foundation & Support

Meta is seeking a Network Engineer to join the Site Services Foundation team, supporting the design, deployment, and operational readiness of enterprise network infrastructure across Meta's global data center and campus environments. In this role, you will work at the intersection of network engineering and site services, collaborating with cross-functional partners in facilities, site operations, hardware engineering, and infrastructure teams to ensure that network technologies are deployed reliably and managed at scale. You will contribute to new product introduction workflows, operational documentation, and continuous improvement initiatives that enable Meta's network infrastructure to evolve alongside the demands of AI, compute, and connectivity at a global scale.

Responsibilities

Drive initiatives to enhance efficiency through tooling improvements in the pre-deployment domain for Backbone, Edge, and Optical Collaborate with internal and cross-functional partner teams to define and automate optimal workflows for network pre-deployment activities Automation and integration into existing tooling ecosystem which involves planning and pre-deployment activities (such as design bootstrapping, engineering design package generation, bill of materials (BOM) creation etc) Proactively find gaps that impact multiple teams/orgs, come up with the execution plan, drive the project, and influence other teams to implement initiatives Work with partner teams, understand their challenges, help them find a solution natively or lead effort working with cross functional teams to deliver systems/platforms/tooling Work with developers and program managers to design and prioritize tooling enhancements and fixes Help with roadmapping to identify and address key challenges Help with recruiting and onboarding of new members to the team. Help them by mentoring and ensuring their quick ramp-up within our environment Perform detailed troubleshooting and engage with cross-functional teams to fix system/tooling issues Drive requirements with cross-functional teams to align tooling development efforts Help make the people around you move faster by identifying issues that are slowing the team down and driving changes to address them

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience 10+ years of experience in the field of automation in the networking domain on large scale networks Knowledge and experience with REST APIs Experience with network devices (routers, switches, load balancers) Experience leveraging AI-assisted tools (e.g., Claude, Copilot, code generation models) for software development and coding efficiency Knowledge and experience with optical, Layer 2, and Layer 3 technologies Knowledge and experience in Python Experience working autonomously within a globally distributed team, managing priorities across time zones Knowledge and experience in C, C++, Go, or Rust Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies MS or Graduate work experience in Computer Science, Computer Engineering, or a related technical discipline Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) 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) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Knowledge of a technology-agnostic approach to engineering Experience with developing or operating distributed systems at scale