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Open nowPosted 325 days ago

AI基础架构工程师-大规模集群方向

sii-czxy/index44 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowAI基础架构工程师-大规模集群方向sii-czxy/index · Shanghai, Chinese Mainland

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  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 325 days ago

The posting

1.大规模集群管理： 负责千卡/万卡级 GPU 集群（如 NVIDIA A100/H100）的日常运维、稳定性保障及容量规划，确保高可用性和高资源利用率。 2.容器化平台建设： 基于 Kubernetes (K8s) 构建和优化 AI 训练平台，负责调度系统（Scheduler）的优化，解决资源隔离、优先级调度及断点续训（Checkpointing）等问题。 3.故障排查与性能优化： 深入排查分布式训练过程中的疑难杂症（如 NCCL Timeout、GPU 掉卡、网络拥塞等），针对训练任务进行软硬件层面的性能调优。 4.底层环境维护： 维护底层驱动（CUDA, cuDNN）、NCCL 通信库及异构硬件的监控体系（Prometheus/Grafana），实现对 GPU、RDMA 网络和存储系统的全链路监控。 5.框架支持与对接： 支持主流深度学习框架（PyTorch, TensorFlow, Megatron-LM 等）在集群上的高效运行，优化 Dataloader 和分布式训练启动流程。

1. 基础架构与容器技术： 精通 Linux 操作系统原理，具有扎实的系统运维经验。 精通 Kubernetes (K8s) 架构及生态，熟悉 Docker/Containerd 容器技术。 有 K8s Operator 开发经验或熟悉 Kubeflow、Volcano 等 AI 调度组件者优先。 2. GPU 与高性能计算经验： 深入理解 GPU 硬件架构，熟悉 NVIDIA GPU (A100/H100) 及其相关技术栈（NVLink, NVSwitch）。 熟悉高性能网络技术，理解 InfiniBand (IB) 或 RoCE 网络架构，熟悉 RDMA 协议及 NCCL 通信原理。 3. 深度学习框架知识： 熟悉 PyTorch 等主流深度学习框架的底层运行机制（如 DDP, FSDP, Pipeline Parallelism）。 具备大规模分布式训练（Multi-Node Multi-GPU）的故障排查经验，能读懂 Python 训练代码并进行基础调试。 4. 编程与开发能力： 熟练掌握 Golang 或 Python 其中一种语言，具备编写自动化运维工具或平台组件的能力。

加分项 ： 大模型经验： 有 LLM（大语言模型）训练集群运维经验，熟悉 Megatron, DeepSpeed 等大模型训练工具。 高性能存储： 熟悉并行文件系统（如 Lustre, GPFS）或高性能对象存储（如 Ceph, MinIO）在 AI 场景下的优化。 监控体系： 搭建过基于 DCGM 的精细化 GPU 监控告警体系。

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