Member of Technical Staff, Performance & Capacity

📋 Description

• Measure the fleet with real workloads to guide capacity decisions
• Own and defend the capacity model with clear assumptions
• Manage workload mix and preemptible fractions over time
• Ensure data path speed with parallel file systems and data locality for GPUs

🎯 Requirements

• 5+ years with GPU and large-scale compute workloads
• System-level GPU performance knowledge for heterogeneous GPUs
• Deep understanding of AI training and inference performance
• Experience owning capacity decisions tied to money
• Familiarity with parallel file systems and high-speed networking (InfiniBand, RDMA)

🎁 Benefits

• Competitive compensation and meaningful equity
• Opportunity to work at AI-native infrastructure scale
• Equal opportunity employer with diverse perspectives