AI/HPC Network Engineer

Summary

Meta's AI Training and Inference Infrastructure is growing exponentially to support ever increasing uses 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 that expects a loss-less fabric interconnect. To improve performance of these systems we constantly look for opportunities across stack: network fabric and host networking, comms lib and scheduling infrastructure.

Required Skills

AI/HPC Network Engineer 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 monitoring systems
• Be oncall to learn from real world production challenges and take the lessons to improve current and future generation products.

Minimum Qualification

Minimum Qualifications:

• Engineering degree, or a related technical discipline or equivalent experience
• Experience coding in languages like Python, C++, Go, etc
• Experience in network automation software leveraging software defined networking principles.
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• Experience in designing, deploying and operating networks at scale.

Preferred Qualification

Preferred Qualifications:

• BS or MS in Computer Science or Computer Engineering
• Expert Knowledge of IB/RDMA/RoCE Networks
• 4+ years of experience working on networks supporting large scale training workloads
• Understanding of routing and switching - hardware design and knowledge of forwarding and data planes
• Understanding of AI training workloads and demands they exert on networks.
• Understanding of RDMA congestion control mechanisms on IB and RoCE Networks.

EOE

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