Ai/Hpc Systems Performance 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 Systems Performance Engineer Responsibilities:

• Active member of a multi-disciplinary team to develop solutions for large scale training systems.
• Responsible for the overall performance of the communication system, including performance benchmarking, monitoring and troubleshooting production issues.
• Identify potential performance issues across the stack: comms lib, RDMA transport, host networking, scheduling and network fabric. Develop and deploy innovative solutions to address the performance issues.

Minimum Qualification
Minimum Qualifications:

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• BS/MS/PhD in relevant fields (EE, CS), with 4+ years work experience.
• Experience with using communication libraries, such as MPI, NCCL, and UCX.
• Experience with developing, evaluating and debugging host networking protocols such as RDMA.
• Experience with triaging performance issues in complex scale-out distributed applications.

Preferred Qualification
Preferred Qualifications:

• 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++.

EOE
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