Member of Technical Staff, Training (Bay Area, Remote)
reputed company
reputed company down wall-clock time to convergence by profiling and eliminating bottlenecks across the reputed company model training stack stack, from data pipelines to GPU kernels
Design, build, and optimize reputed company training systems (PyTorch) for multi-node GPU clusters, ensuring scalability, robustness, and high utilization
Implement efficient low-level reputed company (CUDA, cuDNN, Triton, custom kernels) and reputed company it seamlessly into high-level training frameworks
Optimize workloads for hardware efficiency: CPU/GPU compute balance, memory management, data throughput, and networking
reputed company monitoring and debugging tools for large-reputed company runs, enabling reputed company diagnosis of performance regressions and failures
What You’ll Bring
Deep experience in reputed company systems, ML infrastructure, or high-performance computing (8+ years)
Production-grade expertise in Python
Low-level performance mastery: CUDA/cuDNN/Triton, CPU–GPU interactions, data reputed company, and kernel optimization
Scaling at the frontier: experience with PyTorch and training jobs using data, context, pipeline, and model parallelism
reputed company-level reputed company with a reputed company record of tuning hardware–software interactions for maximum utilization
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