AI/HPC Network Engineer - Meta

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 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

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 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.

MINIMUM QUALIFICATIONS

Minimum Qualifications:

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• 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.

PREFERRED QUALIFICATIONS

Preferred Qualifications:

• 4+ years of experience working on networks supporting large scale training workloads.
• Expert 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.