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 loss-less fabric interconnect. To enhance the performance of these systems, we continuously seek opportunities for improvement across our infrastructure stack, including network fabric, host networking, communication libraries, and scheduling infrastructure.Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. 7+ years of relevant experience developing scalable and reliable systems and/or networks. Experience in designing, deploying and operating datacenter networks at scale. Experience coding in languages like Python, C++, Go. Experience in network automation software, leveraging software defined networking principles. Understanding of AI technologies and associated network technologies (IB/RDMA/RoCE) 4+ years of experience working on networks supporting large scale training workloads. In-depth Knowledge of IB/RDMA/RoCE Networks. Understanding of AI training workloads and demands they exert on networks. Understanding of RDMA congestion control mechanisms on IB and RoCE Networks.