Training / AI Infrastructure

Description

What You’ll Do

• Drive down wall-clock time to convergence by profiling and eliminating bottlenecks across the foundation model training stack 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

What You’ll Bring

• 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

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