Builds and scales distributed training infrastructure for large AI models across multi-node GPU clusters — fault tolerance, checkpointing, recovery, and tooling that makes training reliable, efficient, and cost-effective. Core work spans distributed training frameworks (PyTorch Distributed, DeepSpeed, Megatron-LM, Ray), Kubernetes, and production ML pipelines, hybrid out of Bellevue, WA.
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