Network Engineer, Deployment & Support

Meta Meta · Big Tech · Menlo Park, CA

This role is for a Network Engineer focused on deploying and supporting the network infrastructure for AI workloads. While the role leverages AI tools and requires understanding of AI practices, its core function is network engineering, not building AI models or systems. The primary focus is on scaling and optimizing the network that supports AI, placing it in the 'Serve' stage for the network infrastructure itself.

What you'd actually do

  1. Lead the technical strategy, architecture, and execution for your team’s projects within Edge Network Services (ENS) on network deployment and migrations, ensuring alignment with organizational vision and goals
  2. Provide hands-on technical contributions (design, deployment, migrate, code reviews, troubleshooting, etc.) on large scale complex programs at the experienced engineer level, demonstrating domain expertise and technical impact
  3. Drive strategic planning by building and executing six-month to multi-year roadmaps that support business growth and transformation.
  4. Balance high-level strategic vision with hands-on leadership to deliver results across a broad portfolio of initiatives
  5. Ensure high standards for reliability, scalability, and performance in all deliverables, driving continuous improvement in engineering processes and technical quality

Skills

Required

  • 10+ years of Network Engineering
  • hands-on responsibility for planning, building, deployment or migrating production network environments
  • enterprise and carrier class routing, switching, and optical transport platforms
  • data center design and operational best practices
  • leading and executing network deployment and migration projects
  • scripting languages
  • data center, edge, or large-scale network environments
  • process improvement and systems development
  • automation to streamline repetitive workflows
  • integrating AI tools to optimize/redesign workflows
  • responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • ongoing AI skill development (e.g., prompt/context engineering, agent orchestration)
  • staying current with emerging AI technologies

Nice to have

  • Network certifications such as CCE, CCIE, or JNCIE

What the JD emphasized

  • AI backbone network
  • AI network infrastructure
  • AI tools to optimize/redesign workflows
  • responsible, ethical AI practices
  • AI skill development