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Open nowPosted 110 days ago

大模型训练工程专家

weikezhijia/msxf4 open roles

Where
Chengdu, Sichuan, Chinese Mainland
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Your applicationOpen now大模型训练工程专家weikezhijia/msxf · Chengdu, Sichuan, 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.

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 110 days ago

The posting

大规模分布式训练系统架构与优化：负责设计、搭建和维护千卡级别的高性能分布式训练集群。深入优化分布式训练框架（如DeepSpeed、Megatron-LM、PyTorch DDP/FSDP），解决大规模训练中的计算、通信、存储瓶颈问题，提升训练效率与稳定性。 模型训练全流程管理：主导从数据预处理、模型加载、训练启动、监控告警到Checkpoint保存与恢复的全流程。针对训练过程中出现的Loss异常、梯度爆炸/消失、模型不收敛等问题进行快速定位与修复。 硬件与算力适配：针对GPU计算芯片进行底层算子优化、通信库调优（如NCCL），最大化硬件利用率。 训练框架与工具链开发：开发或改进内部训练框架、任务调度系统、资源管理系统和自动化运维工具，提升团队研发效率。参与开源社区（如PyTorch、TensorFlow）的贡献，保持技术领先性。 前沿技术探索与落地：跟踪并研究MoE（混合专家模型）、序列并行、张量并行、流水线并行、重计算、ZeRO优化等前沿技术，并将其工程化落地到实际训练任务中，持续降低训练成本、缩短训练周期。

硬性门槛： 计算机科学、人工智能、数学、物理等相关专业硕士及以上学历，博士优先。 3年以上大规模分布式系统或高性能计算（HPC）相关工作经验，其中至少2年专注于大模型训练工程领域。 主导过千卡以上规模、百亿参数级别的基础大模型（如LLM、多模态模型）的完整训练过程，并有成功的上线经验。 核心技术栈： 编程语言：精通Python和C++，具备出色的工程编码能力和系统级调试能力。 深度学习框架：深入理解PyTorch源码，熟悉TensorFlow、JAX等至少一种框架。 分布式技术：精通MPI、NCCL、Gloo等通信库，熟悉分布式一致性、容错、负载均衡等原理。 并行策略：对数据并行、模型并行（张量/流水线/序列并行）、混合并行有深刻理解和丰富的实战调优经验。 系统与工具：熟悉Linux内核、网络协议（RDMA/IB）、高性能存储（如Lustre、GPFS）。熟练使用Docker、Kubernetes、Slurm等容器化和调度工具。 问题解决与创新能力： 具备极强的故障排查能力，能快速定位并解决训练中断、性能抖动、显存溢出等复杂问题。 能够从系统层面思考问题，提出创新性的优化方案，例如通过改进数据加载器、优化通信拓扑来提升整体吞吐量。 软性素质： 强烈的自驱力和结果导向意识，能在高压下推动项目进展。 优秀的跨团队沟通与协作能力，能与算法研究员、硬件工程师、运维团队高效配合。 具备技术领导力，能够指导初中级工程师成长，并沉淀技术文档与最佳实践。 加分项： 在顶级会议（如OSDI、SOSP、SC、MLSys、NeurIPS、ICML）发表过关于大规模分布式训练或系统优化的论文。 是知名开源项目（如DeepSpeed、Megatron-LM、vLLM、Ray）的核心贡献者或维护者。 有GPU/TPU/NPU等AI芯片的底层驱动或算子开发经验。 熟悉模型压缩、量化、蒸馏等推理优化技术，有训练-推理一体化经验。 有参与建设万卡级别算力中心或超算集群的经验。

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