Network Engineer, Deployment & Support
Meta
This job at a glance
- Who can apply
- New grads, no experience needed · Bachelor's degree
- Must know
- NetworkingTroubleshootingStakeholder managementOperations
- Work
- On-site in Newton County
- Pay
- Not stated by the employer.
- Skill demand
- Networking86%Troubleshooting64%Operations51%Stakeholder management14%of 100 similar entry-level jobs posted in the US this week
- Listed
- Oct 27, 2026 · still open
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About the Role
Meta's Edge & Network Services team is responsible for the deployment and support of all production networks (Edge, Backbone, Optical, and Datacenter) as well as our content delivery network across the globe. This team works with the latest network technologies and solves complex industry problems.
Key Responsibilities
- Participate in the design engineering and implementation of large-scale data center networks across multiple geographic locations.
- Research, develop, and deploy technologies and network topologies to evolve and scale data centers and optimize deployments.
- Initiate and lead strategic initiatives driving efficiency, innovation, and standardization of data centers.
- Work closely with cross-functional teams and external vendors to develop new features and influence the future of networking.
- Influence future iterations of data center and network designs for seamless integration of new infrastructure.
- Actively participate in the New Product Introduction (NPI) process for network infrastructure.
- Partner with software teams to develop and implement tooling for provisioning, managing, monitoring, and troubleshooting networks.
- Generate network infrastructure design packages for deployment teams, collaborating with network engineering, data center design, and data center connectivity teams.
- Build and nurture business relationships with key stakeholders, partners, and vendors.
- Develop automation and tooling to drive consistency and efficiency in network design, deployment, and operations.
- Perform site visits and work cross-functionally with construction, design architects, and contractors to validate cabling infrastructure and network builds according to Meta standards.
- Maintain technical reference, process, and training documentation.
- 25% to 40% of travel required based on project demand.
Requirements & Tech Stack
- Experience with leveraging AI to drive area efficiencies for improved reliability, quality, and speed of deployments.
- In-depth experience with designing and deploying large-scale data center network infrastructure.
- Demonstrated knowledge of structured cabling and fiber optic infrastructure including design and installation best practices, industry standards, application, and limitations.
- Knowledge of the data center design, construction, and start-up/commissioning processes.
- Experience leading cross-functional teams and driving large-scale data center projects.
- Track record of solving problems, setting strategic direction, and executing tactically.
- Working knowledge of internet networking technologies (TCP/IP, routing, switching) and optical transport technologies.
- Knowledge of enterprise and provider level network hardware platforms (IP + Optical).
- Organizational, multitasking, prioritization, and communication experience.
Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- Experience leading and/or managing technical teams.
- In-depth knowledge of network protocols (TCP/IP, BGP, IS-IS, etc.).
- Knowledge of network tooling and automation systems.
- In-depth knowledge of inside/outside plant cable design and dark fiber builds.
- Experience working on a global team in a matrixed environment with limited supervision.
- Experience working with on-site construction teams and managing contractor and vendor relationships.
- Data center design or relevant telecommunications certifications.
- 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.