Network Engineer, Deployment & Support
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Before you apply
Salary$162k – $227k
LocationMenlo Park, CA; Seattle, WA
Typefull-time
SeniorityLead
Experience10+ yrs
DepartmentData Center
EquityIncluded
Company size10,000+ people
PostedOct 14, 2026 · 3w ago
Verified live1m ago
At a glanceSummarised by Seekless from the posting.
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
10+ years of Network Engineering, including hands-on responsibility for planning, building, deployment or migrating production network environments
Experience with leveraging AI to drive areas of efficiency for improved reliability, quality, and speed of deployments
In-depth experience with designing and deploying large-scale data center network infrastructure
Knowledge of the data center design, construction, and startup/commissioning processes
Experience leading cross-functional teams and driving large-scale data center projects
Track record of defining technical strategy and delivering measurable outcomes on complex infrastructure projects
Working knowledge of internet networking technologies (TCP/IP, routing, switching), and optical transport technologies
Knowledge of enterprise and provider-level network hardware platforms (IP and Optical)
Organizational, multitasking, prioritization, and communication experience
Experience in leading and/or managing technical teams
Data center design or relevant telecommunications certifications
Knowledge of network tooling and automation systems
Experience working on a global team in a matrixed environment with demonstrated ability to independently drive priorities and decisions
In-depth knowledge of network protocols (TCP/IP, BGP, IS-IS, etc.)
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Skills
TCP/IP
routing
switching
optical transport
IP
Optical
network hardware platforms
BGP
IS-IS
About the role
Meta is looking for a Network Deployment & Support Engineering lead to join the team responsible for planning and executing network deployments and migrations to build large-scale AI backbone network infrastructure. In this role, you will have a unique opportunity to work on cutting-edge Optical and IP networking technologies, owning the deployment planning, implementation, and scaling of Meta’s AI network infrastructure.
Responsibilities
•
Drive strategic planning by building and executing six-month to multi-year roadmaps that support business growth and transformation. Balance high-level strategic vision with hands-on leadership to deliver results across a broad portfolio of initiatives
•
Lead the technical strategy, architecture, and execution for team’s projects within Edge Network Services (ENS) on network deployment and migrations, ensuring alignment with organizational vision and goals
•
Collaborate cross-functionally across Network Engineering, Data Center, Edge, and planning teams to ensure the successful execution of shared goals on network capacity and reliability
•
Provide technical contributions (design, deployment, migrate, etc.) on large-scale, complex programs at the experienced engineer’s level, demonstrating domain expertise and technical impact
•
Ensure high standards for reliability, scalability, and performance in all deliverables, driving continuous improvement in engineering processes and technical quality
•
Deploy, migrate, and support our global production network, including edge, backbone, and data center infrastructure
•
Manage key vendor relationships, including contract negotiations, pricing, and evaluation of current and potential partnerships
•
Manage partner engineering team relationships to enable the team to effectively meet business needs
•
Mentor and onboard new team members, raising the team’s overall deployment capabilities and fostering team collaboration and knowledge sharing
•
Demonstrate leadership behaviors by helping others, showing respect, connecting people, and driving collaboration within the team and the organization
•
Travel required, approximately 10% to 30%
Minimum 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 Network Engineering, including hands-on responsibility for planning, building, deployment or migrating production network environments
•
Experience with leveraging AI to drive areas of efficiency for improved reliability, quality, and speed of deployments
•
In-depth experience with designing and deploying large-scale data center network infrastructure
•
Knowledge of the data center design, construction, and startup/commissioning processes
•
Experience leading cross-functional teams and driving large-scale data center projects
•
Track record of defining technical strategy and delivering measurable outcomes on complex infrastructure projects
•
Working knowledge of internet networking technologies (TCP/IP, routing, switching), and optical transport technologies
•
Knowledge of enterprise and provider-level network hardware platforms (IP and Optical)
•
Organizational, multitasking, prioritization, and communication experience
Preferred qualifications
•
Experience in leading and/or managing technical teams
•
Data center design or relevant telecommunications certifications
•
Knowledge of network tooling and automation systems
•
Experience working on a global team in a matrixed environment with demonstrated ability to independently drive priorities and decisions
•
In-depth knowledge of network protocols (TCP/IP, BGP, IS-IS, etc.)
•
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
•
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
•
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies