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 training accelerators like GPUs together. In addition, we need to ensure that the network is running smoothly and meets stringent performance and availability requirements of RDMA workloads. These workloads expect a loss-less fabric interconnect with minimal latency. To improve performance of these systems we constantly look for opportunities across stack: network fabric and host networking, comms lib and scheduling infrastructure.Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Experience with developing, evaluating and debugging host networking protocols such as RDMA. 10+ years of experience in designing, deploying and operating networks. Experience with triaging performance issues in complex scale-out distributed applications. Experience with developing communication libraries, such as MPI, NCCL, and UCX. Understanding of AI training workloads and demands they exert on networks. Understanding of RDMA congestion control mechanisms on IB and RoCE Networks. Understanding of the latest artificial intelligence (AI) technologies. Experience with machine learning frameworks such as PyTorch and TensorFlow Experience in developing systems software in languages like C++