Design, orchestrate, and optimize large-scale LLM pre-training runs across 1,000+ GPUs using PyTorch/DeepSpeed/Megatron-LM. Improve networking (InfiniBand/RDMA) and memory management, implement 3D parallelism, and automate checkpointing and failure recovery for month-long training jobs on SLURM or Kubernetes GPU clusters.
We are seeking a highly skilled LLM Pre-training & Distributed Systems Engineer. This role is essential for orchestrating large-scale machine learning training runs and optimizing distributed infrastructure. The ideal candidate will have a deep understanding of GPU clusters and extensive experience in system engineering to ensure efficient and reliable training processes.
Responsibilities:
- Orchestrate distributed training runs across 1,000+ GPUs using PyTorch, DeepSpeed, or Megatron-LM.
- Optimize networking (InfiniBand/RDMA) and memory management to prevent out-of-memory errors.
- Automate checkpointing and failure recovery during month-long training runs.
Required Skills:
- Deep expertise in 3D parallelism (Data, Tensor, Pipeline).
- Experience managing SLURM or Kubernetes-based GPU clusters.
- Strong systems engineering background (C++, CUDA, Python).
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