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Open nowPosted 94 days ago

大模型训推性能优化工程师 - AI算力基础设施

ByteDance19,257 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now大模型训推性能优化工程师 - AI算力基础设施ByteDance · Beijing, Chinese Mainland

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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. ByteDance postings stay open a median of 16 days.

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 94 days ago

ByteDance median: 16 days open

The posting

团队介绍：字节跳动基础设施部门，负责字节跳动的全栈算力基础设施，从芯片服务器产研、到超大规模数据中心，到传统云平台和如今的AI Native Cloud，全方位为抖音、豆包、今日头条、飞书、火山引擎等各类产品提供领先、稳定的百万量级大规模算力基础设施服务。我们的领域涵盖数据中心建设、内核操作系统开发、服务器集群管理、高速网络通信、EB级数据存储体系、搜索型数据库探索、基于LLM的AI基础设施的研发调度与治理等，致力于打造业界领先的、面向LLM的AI云原生基础设施架构与产品矩阵。

1、参与字节跳动大模型训练与推理全链路性能优化，聚焦千亿/万亿参数规模模型的训练吞吐、推理时延与资源效率提升；参与模型优化方向，LLM与多模态模型推理优化，包括混合精度量化（INT4、INT8、FP8）、稀疏化、Sparse Attention等稀疏注意力机制优化技术，平衡推理性能、模型精度与资源开销；参与主流训推框架（PyTorch、vLLM、SGLang等）与自研框架的适配、优化与二次开发，落地投机采样（Speculative Decoding）算法优化，优化专家路由策略与负载均衡机制，设计计算-通信精细化调度与重叠（Overlap）等方案，提升MOE架构模型推理吞吐量； 2、参与通信优化方向：AI框架跨节点/集群通信性能攻坚，优化RDMA/TCP协议栈传输机制、低延迟序列化方案与通信拓扑设计，突破分布式场景下的通信瓶颈； 3、参与计算优化方向：Transformer核心算子、卷积、矩阵乘加（GEMM）等AI核心算子优化，基于Triton/MLIR编译框架实现算子融合、向量化执行与编译优化； 4、参与GPU/NPU等异构算力平台的性能建模、性能剖析与代码级调优，适配GPU CUDA/ROCm及国产AI芯片专用指令集，最大化硬件算力利用率； 5、参与构建AI Infra性能Benchmark体系，形成量化性能标尺与竞争力评估模型。

1、2027届获得本科及以上学历，计算机、软件工程、电子信息、微电子等相关专业优先； 2、掌握扎实的计算机基础与算法功底，熟练掌握C/C++、Python、Go中的至少一种编程语言； 3、了解LLM/VLM/AIGC等AI领域基础原理与典型模型架构，理解训练与推理的基本流程； 4、有PyTorch等主流深度学习框架的使用经验，参与过模型训练、参数调优或框架组件的二次开发； 5、对异构计算、高性能网络或存储IO的性能分析具备理论基础与研究兴趣，愿意深入钻研性能优化工具的原理； 6、具备良好的沟通协作能力、学习能力与工程责任心。

加分项 1、具备CUDA/Triton等GPU编程经验，或熟悉TensorRT/CUTLASS等性能加速工具，理解其加速原理； 2、在学术论文、课程设计或竞赛中涉及AI系统性能分析相关工作，如训练瓶颈定位、通信优化、并行策略、算子融合、量化与精度调优等； 3、有开源社区贡献经历，参与过AI框架、推理引擎或性能优化工具的代码提交与维护； 4、接触过模型量化、精度校准、并行训练、通信优化、投机采样、MOE路由等相关方向； 5、关注国产AI加速卡技术生态，具备相关文献研究或实验环境下的应用测试经验； 6、熟悉主流AI加速硬件（NVIDIA/AMD GPU、国产AI芯片）的架构特性，或使用过PyTorch Profiler、NVIDIA Nsight等性能分析工具。

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