AI/HPC Network Performance Engineer

Meta's AI Training and Inference Infrastructure is growing exponentially to support ever increasing use cases of AI. This results in a dramatic scaling challenge that our engineers have to deal with on a daily basis. We need to build and evolve our network infrastructure that connects myriads of GPUs together. In addition, we need to ensure that the network is running smoothly and meets stringent performance and availability requirements of RDMA workloads that expects a lossless fabric interconnect. To improve performance of these systems we constantly look for opportunities across our infrastructure stack: network fabric and host networking, comms lib and scheduling infrastructure.

Design, develop, test and operate networking systems to support large scale AI training jobs Research, develop and deploy numerous technologies and network topologies in order to evolve and scale our AI 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 Define and develop optimized network automation tools and systems, including configuration, provisioning, monitoring, alarming, auto-remediation and more Be oncall to learn from real world production challenges and take the lessons to improve current and future generation products Provide guidance on network architecture including scale-up and scale-out topologies, transport protocols, and performance optimization techniques

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 8+ years of experience in system performance engineering, network infrastructure engineering, or a related field within large-scale distributed computing or HPC environments Experience coding in languages like Python, C++, Go Experience in designing, deploying and operating datacenter networks at scale Experience in network automation software leveraging software defined networking principles Understanding of AI training workloads and demands they exert on networks Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) 4+ years of experience working on networks supporting large scale training workloads Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience working with IB/RDMA/RoCE Networks Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Understanding of RDMA congestion control mechanisms on IB and RoCE Networks Experience with scale-up and scale-out network fabric architectures