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Open nowPosted 66 days ago

大模型Infra工程师 (VLM训练)

shengshu/index98 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now大模型Infra工程师 (VLM训练)shengshu/index · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. shengshu/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 66 days ago

shengshu/index median: 1 days open

The posting

负责支撑百亿/千亿级 VLM 从零开始的预训练。直面万卡级集群的通信瓶颈与显存墙，深入分布式训练内核，从第一性原理出发设计并落地业界领先的并行策略与通信调度方案。

1. 分布式并行架构设计与演进：主导设计并持续优化 4D 并行（DP/PP/TP/CP）策略。深入理解并落地 DeepEP、Dual Pipe、ZeRO 等核心机制，在多机多卡环境下实现通信与计算的高度重叠，逼近集群线性加速比。 2. 集群通信效率极致优化：从拓扑亲和性与通信原语调度层面，对分布式训练中的集体通信模式进行深度调优。通过计算通信重叠、通信分组优化等手段，将集群通信效率逼近硬件带宽上限。 3. 显存与算力利用率的极致压榨：围绕 MFU（Model FLOPS Utilization） 和显存利用率两大核心指标，持续进行系统级调优。结合 Context Parallelism 等并行策略，在多卡环境下实现显存、带宽、算力三者之间的最优平衡。 4. 训练稳定性与全链路 Profiling：建立系统级性能看护体系，通过端到端 Profiling 精准定位慢节点、通信拥塞及负载不均等系统性瓶颈。从集群调度、存储 I/O 到计算图执行进行全链路分析，保障数月级长稳训练及故障快速恢复。

1. 原理级认知：对分布式训练有极深的理解。不仅熟悉主流并行策略的工程实现，更能从数学与系统层面推导通信量、显存峰值与算力之间的制约关系，具备从第一性原理出发设计或改造并行方案的能力。 2. 工程实战：精读过 Megatron-LM 或 DeepSpeed 核心源码。有从零构建或深度定制 3D/4D 并行框架的实际经验，对 PyTorch 分布式模块的底层执行逻辑有系统性认知。 3. 系统级性能调优能力：具备大规模集群下的端到端调优经验，能够从计算图、内存分配、网络通信、存储 I/O 等多维度进行联合优化。对 MFU 的拆解与提升有系统方法论。 4. 异构计算认知：熟悉现代 GPU 架构特性（SM 调度、Tensor Core、显存层级），对异构计算场景下的负载均衡有实战经验。 5. 加分项： - 有千卡/万卡级以上 VLM 预训练成功落地经验 - 有 Megakernel 开发或深度优化经验 - 有多框架（PyTorch/JAX）或多芯片（华为昇腾/AMD MI 系列）适配经验

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