Network Production Engineer (university Grad)

Meta Meta · Big Tech · Menlo Park, CA

Meta is seeking a University Grad Network Production Engineer to ensure the reliability, robustness, and scalability of their network infrastructure. The role involves building, managing, and maintaining multi-vendor networks, developing automation systems, designing new architectures, and analyzing data to resolve network issues. The position also requires experience with AI tools for workflow optimization and adherence to ethical AI practices.

What you'd actually do

  1. Build experience with Backbone, Data Center, and Network Infrastructure Engineering teams through rotational experiences during your first year
  2. Build, manage and maintain multi-vendor, multi-protocol data center and backbone networks
  3. Develop optimized network monitoring systems
  4. Design, and deploy new network architectures
  5. Develop automated methods to mitigate and remediate network events

Skills

Required

  • Computer networks
  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • Coding in at least one language (Python/Golang/Rust/C++)
  • UNIX, TCP/IP, or similar
  • Tunneling protocols MPLS, GRE, IPnIP
  • Routing protocols BGP, OSPF and ISIS
  • Switching and Routing concepts and implementation

Nice to have

  • DHCP, ARP
  • routing protocols such as OSPF, BGP, HTTP/HTTPS
  • AI tools to optimize/redesign workflows
  • responsible, ethical AI practices
  • AI skill development (e.g., prompt/context engineering, agent orchestration)
  • emerging AI technologies

What the JD emphasized

  • ensure that the network is reliable, robust and can scale to meet the challenges that serving over a billion users presents
  • Automation and continuous improvement are the keys to meeting our demands
  • Develop automated methods to mitigate and remediate network events
  • 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