Note: The job is a remote job and is open to candidates in USA. Meta is building the next generation of AI infrastructure to power large-scale machine learning workloads, and the reliability of that infrastructure depends on reliable, high-performance network engineering. In this role, you will lead the strategy and execution for AI network repair and remediation programs, ensuring that the high-performance fabrics underpinning Meta's AI training and inference clusters remain operational, resilient, and optimized.
Responsibilities
- Define and drive the long-term strategy for AI network repair and remediation programs across large-scale data center environments supporting machine learning workloads
- Lead root cause analysis and resolution of complex network faults affecting high-performance AI training and inference fabrics, including RDMA, high-speed Ethernet, and optical interconnect layers
- Develop and champion novel approaches to network fault detection, automated remediation, and repair workflow optimization for AI cluster infrastructure
- Partner with hardware, software, and data center operations teams to align network repair programs with AI infrastructure deployment roadmaps and capacity plans
- Establish and refine operational frameworks, runbooks, and tooling for network repair at scale, reducing mean time to repair across AI fabric environments
- Identify systemic reliability risks in AI network infrastructure and drive cross-functional initiatives to address them before they impact production workloads
- Influence the design of next-generation AI network architectures by contributing repair and reliability insights to hardware and topology decisions
- Leverage AI-driven analytics and automation tools to redesign repair workflows, accelerating fault identification and resolution across distributed network environments
- Build and maintain strategic relationships with internal engineering, operations, and vendor partners to ensure repair programs scale with AI infrastructure growth
- Communicate program status, risk, and strategic recommendations to engineering leaders and cross-functional stakeholders through structured reporting and executive briefings
Skills
- Experience influencing technical direction and organizational strategy through data-driven analysis, written proposals, and stakeholder alignment across engineering and operations teams
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Experience leading cross-functional programs that span network operations, hardware deployment, and infrastructure reliability at data center scale
- Experience developing and driving strategy for network fault management, repair automation, or remediation programs in production environments
- Experience designing, deploying, or operating high-speed network fabrics used in AI or machine learning infrastructure, including technologies such as RDMA over Converged Ethernet, InfiniBand, or high-density optical interconnects
- 12+ years of experience in network engineering, with a focus on large-scale data center or high-performance computing network environments
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Experience with network telemetry platforms, observability tooling, or AI-assisted anomaly detection applied to large-scale fabric environments
- Experience building or scaling repair operations programs, including workforce planning, tooling development, and process standardization across multiple data center sites
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Track record of contributing to network hardware or topology design reviews, translating operational repair insights into upstream engineering improvements
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Familiarity with AI accelerator interconnect architectures and the network reliability requirements of distributed training workloads at hyperscale
Benefits
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