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Research Infrastructure Engineer, Training Systems

295k – 380kSan Francisco, CAHybrid
Summary

Builds and maintains infrastructure for large-scale ML model training and experimentation. Designs APIs, improves reliability and performance of training pipelines, and debugs issues across Python, PyTorch, distributed systems, GPUs, and networking.

About the role

Responsibilities

  • Build and maintain infrastructure for large-scale model training and experimentation.
  • Design APIs and interfaces that make complex training workflows easier to express and harder to misuse.
  • Improve reliability, debuggability, and performance across training and data pipelines.
  • Debug issues spanning Python, PyTorch, distributed systems, GPUs, networking, and storage.
  • Write tests, benchmarks, and diagnostics that catch meaningful regressions.
Skills
PyTorchPythonDistributed SystemsGPUsKubernetesML Training InfrastructureData PipelinesNetworkingStorage SystemsPerformance Optimization
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