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Training / AI Infrastructure

Genesis

San Francisco, CAFull-timePosted 4mo agoStill listed 1w ago

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At a glance

Compensation
No compensation found
Location
San Francisco, CA
Schedule
Full-time
Work Authorization
Not specified

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Job overview

The role focuses on accelerating model training by profiling and removing bottlenecks across the entire stack, from data pipelines to GPU kernels. It involves designing, building, and optimizing distributed PyTorch training systems for multi‑node GPU clusters, ensuring scalability, robustness, and high utilization.

Skills & qualifications

RequiredNice to have

Skills

High Performance ComputingCudnnData ManagementDebuggingPerformance TuningScalabilityRobustness TestingDistributed SystemsML InfrastructurePythonCUDATritonCPU‑GPU InteractionsData MovementKernel OptimizationPyTorch

Qualifications

8+ Years Experience

Full 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

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