Principal ML Engineer - Large Scale Training Performance Optimization
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The role
Lead distributed training of large-scale models across multi-GPU systems to convergence.
Design and optimize end-to-end training pipelines, leveraging data, tensor, pipeline, and expert parallelism (including ZeRO) to scale out.
Implement algorithmic and kernel-level optimizations to improve training throughput and efficiency.
Stay current with the latest training methods and contribute changes to open-source projects.
Collaborate across teams and stakeholders to influence the direction of the AI platform while communicating results clearly.
Requires a master's or PhD in CS/AI with hybrid (partial on-site) work in San Jose, CA or Bellevue, WA.
Design and optimize end-to-end training pipelines, leveraging data, tensor, pipeline, and expert parallelism (including ZeRO) to scale out.
Implement algorithmic and kernel-level optimizations to improve training throughput and efficiency.
Stay current with the latest training methods and contribute changes to open-source projects.
Collaborate across teams and stakeholders to influence the direction of the AI platform while communicating results clearly.
Requires a master's or PhD in CS/AI with hybrid (partial on-site) work in San Jose, CA or Bellevue, WA.
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