Production Engineer, Network AI Lead

The scale of the network and its continuous expansion presents an opportunity to work on and solve interesting engineering challenges in the data center network domain. We create new and innovative ways of designing and operating our global data center networks and do it at scale with efficiency.

Production Network Engineers at Meta are hybrid software and network engineers who design, build, and operate our worldwide network. This team owns the complete lifecycle of the network, which includes areas of planning, design, product definition, QA, deployment, and monitoring. Simple, elegant, and scalable network design, automation, and data analytics are the key to meeting our demands. In this role, you will be part of a team that is responsible for conceiving design solutions, developing and deploying network software, systems, and tools that keep the network operating at maximum reliability, scalability, and efficiency.

This role offers an opportunity to solve the scaling challenges of supporting billions of people using our family of apps; to cutting-edge challenges in AI workloads that power new Meta products.

Responsibilities

Establish and implement global best practices and design new, scalable network solutions Conceptualize, build, and maintain automation and tools to support new product introductions, network deployment, release engineering, and operations Design and develop solutions that scale across a variety of hardware platforms of network equipment Lead enhancements of automation for continuous integration, validations, testing infrastructure, release, and configuration management across our global backbone, data center, and edge networks Work closely with our hardware, software, and sourcing teams to develop new networking solutions and influence the future of networking and its associated infrastructure Conduct thorough investigations into complex technical issues across networks, ranging from automated tooling to hardware failures and network issues Develop operational process improvements and implement them in scalable, automated workflows to enhance operational efficiency Help increase operational efficiency between peers and cross-functional teams by identifying roadblocks, designing and delivering automation solutions, and driving change Proactively find gaps that impact multiple teams, come up with the execution plan, drive the project, and influence other teams to reach their goals Participate in an oncall rotation to learn from real-world production challenges and take the lessons to improve current and future generation products Contribute to team growth and development through peer mentorship

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 8+ years of relevant experience in developing scalable and reliable systems and/or networks Experience coding in higher-level languages (e.g., Python, C++, Go, Rust, etc.) Experience learning software, frameworks, and APIs Experience in the configuration and maintenance of network devices and NMS systems, or applications such as web servers, load balancers, relational databases, storage systems, and messaging systems Experience developing and understanding network device configuration for at least one vendor (Juniper, Cisco, Arista, Brocade, etc.) Experience with physical network infrastructure design including rack solutions, fiber connectivity, and automated design synthesis for AI or GPU cluster fabrics, ring interconnects, or backbone aggregation topologies Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience with centralized or hybrid routing design, including synthesis and verification frameworks for large-scale multiplanar or disaggregated network topologies Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience integrating AI-assisted workflows or automation tooling to accelerate network design, testing, or operational processes with measurable efficiency or quality improvements Experience qualifying new network hardware platforms and optics through structured NPI frameworks, including end-to-end validation and targeted feature qualification