Member of Technical Staff, Training

Role Description

• Drive down wall-clock time to convergence by profiling and eliminating bottlenecks across the foundation model training stack, from data pipelines to GPU kernels.
• Design, build, and optimize distributed training systems (PyTorch) for multi-node GPU clusters, ensuring scalability, robustness, and high utilization.
• Implement efficient low-level code (CUDA, cuDNN, Triton, custom kernels) and integrate it seamlessly into high-level training frameworks.
• Optimize workloads for hardware efficiency: CPU/GPU compute balance, memory management, data throughput, and networking.
• Develop monitoring and debugging tools for large-scale runs, enabling rapid diagnosis of performance regressions and failures.

Qualifications

• Deep experience in distributed 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 movement, and kernel optimization.
• Scaling at the frontier: experience with PyTorch and training jobs using data, context, pipeline, and model parallelism.
• System-level mindset with a track record of tuning hardware–software interactions for maximum utilization.