AI/HPC System Performance Engineer

Meta Platforms, Inc.•Austin, TX

34d

About The Position

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, communications lib and scheduling infrastructure.

Requirements

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

Nice To Haves

Experience with developing communication libraries, such as Message Passing Interface, NCCL, and UCX
Understanding of AI training workloads and demands they exert on networks
Understanding of RDMA congestion control mechanisms on InfiniBand 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++

Responsibilities

Lead multi-disciplinary teams to develop solutions for large scale training systems. Assess trade-offs of various solutions and make pragmatic decisions
Ensure timely milestone delivery with teamwork and close collaboration
Responsible for the overall performance of the communication system, including performance benchmarking, monitoring and troubleshooting production issues
Defining technical vision and driving a multi-year roadmap to make progress towards the related objectives
Work with cross functional teams and provide guidance on the AI network architecture including topologies, transport, congestion control techniques

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