AI​/HPC Systems Performance Engineer

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.

• Collaborate with hardware and software teams to optimize end-to-end communication pathways for large-scale distributed training workloads, ensuring seamless integration between compute, storage, and networking components
• Design, implement, and validate new collective communication algorithms tailored for AI/HPC workloads, leveraging RDMA and advanced networking technologies to maximize throughput and minimize latency
• Develop and maintain automated performance testing frameworks for continuous benchmarking of communication libraries and RDMA transport layers, enabling rapid identification of regressions and bottlenecks
• Analyze and profile communication patterns in real-world training jobs, using telemetry and tracing tools to uncover inefficiencies and recommend architectural improvements
• Drive adoption of best practices for scalable, fault-tolerant communication in production environments, including tuning RDMA parameters, optimizing network fabric configurations, and ensuring robust error handling
• Work closely with vendors and internal teams to evaluate and integrate new hardware features (e.g., NICs, switches, accelerators) that can enhance communication performance for AI/HPC clusters
• Contribute to documentation and knowledge sharing by authoring technical guides, performance reports, and internal wiki pages to educate peers and stakeholders on communication system optimizations
• Participate in code reviews and design discussions to ensure high-quality, maintainable solutions that meet the evolving needs of large-scale AI/HPC infrastructure

• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
• Bachelor's degree in Computer Science, Computer Engineering, or other relevant technical field, with 2+ 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

• 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 Tensor Flow
• Experience in developing systems software in languages like C++

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