AI/HPC Network Performance Engineer

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Meta is seeking an AI/HPC Network Performance Engineer to design, build, and operate networking systems that connect thousands of GPUs for scale AI training, onsite in Menlo Park, CA, with a focus on lossless RDMA fabrics and scalable topologies. The role demands 8+ years in system performance or network infrastructure within large distributed or HPC environments, hands-on data center networks at scale, and proficiency in Python, C++, or Go, plus network automation and SDN. You should understand AI training workloads, IB/RDMA/RoCE networks, and be comfortable on call to translate production challenges into improvements. To apply, highlight measurable outcomes such as efficiency gains, bottleneck removal, successful automation, and concrete topology experience, plus cross functional collaboration.

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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.

Responsibilities
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

Qualifications
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