MTS, Training
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Genesis
San Carlos, CA, US
Summary
Optimize the foundation model training stack by eliminating bottlenecks and designing scalable distributed training systems on multi-node GPU clusters. Develop low-level CUDA kernels and monitoring tools to improve hardware efficiency and diagnose performance regressions.
Job Description
What You’ll Do
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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
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Design, build, and optimize distributed training systems (PyTorch) for multi-node GPU clusters, ensuring scalability, robustness, and high utilization
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Implement efficient low-level code (CUDA, cuDNN, Triton, custom kernels) and integrate it seamlessly into high-level training frameworks
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Optimize workloads for hardware efficiency: CPU/GPU compute balance, memory management, data throughput, and networking
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Develop monitoring and debugging tools for large-scale runs, enabling rapid diagnosis of performance regressions and failures
What You’ll Bring
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Deep experience in distributed systems, ML infrastructure, or high-performance computing (8+ years)
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Production-grade expertise in Python
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Low-level performance mastery: CUDA/cuDNN/Triton, CPU–GPU interactions, data movement, and kernel optimization
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Scaling at the frontier: experience with PyTorch and training jobs using data, context, pipeline, and model parallelism
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System-level mindset with a track record of tuning hardware–software interactions for maximum utilization
