Training / AI Infrastructure

Job 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