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Open nowPosted 232 days ago

AI训推优化实习生-算力与AI基础设施

ByteDance19,260 open roles

Where
Hangzhou, Zhejiang, Chinese Mainland
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Your applicationOpen nowAI训推优化实习生-算力与AI基础设施ByteDance · Hangzhou, Zhejiang, 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.

11.3% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 27 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.3%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 232 days ago

ByteDance median: 27 days open

The posting

ByteIntern：面向2027届毕业生（2026年9月-2027年8月期间毕业），为符合岗位要求的同学提供转正机会。 团队介绍：字节跳动云基础设施部门，通过云技术管理着百万量级的服务器构成的超大数据中心。我们通过深度优化千万级容器实例与算力优化，搭建EB级数据存储治理体系，探索新一代搜索型数据库与大规模AI集群下的高速网络通信，我们积极拥抱开源和创新的软硬件架构，致力于构建业界领先、稳定、高可用的面向LLM的AI云原生的基础设施架构与产品矩阵，为整个公司的业务和客户发展保驾护航。

1、参与构建AI Infra性能Benchmark体系，设计量化性能标尺与竞争力评估模型，实现算力资源的精准度量与效能评估； 2、参与LLM/VLM/AIGC模型训练/推理优化，开展底层技术栈的性能剖析与代码级优化，提升模型的训练吞吐量与推理响应速度，优化资源占用效率。

1、2027届本科及以上学历在读，计算机、软件工程、电子信息等相关专业； 2、掌握C/C++、Python、Go中至少一种编程语言，具备课程设计、科研项目或开源项目的开发与调试实践经验，能够清晰阐述技术实现思路与问题解决过程； 3、了解LLM/VLM/AIGC等AI领域的基础原理与典型模型架构，在学术研究、竞赛或课程学习中接触过相关模型，具备探究其运行机制的技术好奇心； 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（如模型代码修改、训练流程优化等）； 5、对异构计算、网络传输或存储IO的性能分析具备理论基础与研究兴趣，有意愿深入钻研性能分析工具的原理与优化方法。

加分项： 1、在学术论文、课程设计中涉及AI系统性能分析相关研究，如模型训练瓶颈定位、计算资源调度优化等； 2、有开源社区贡献经历，参与过AI框架或性能优化工具的代码提交与维护； 3、基于PyTorch等框架完成过模型训练/推理项目，并实施过速度优化或资源占用率改进措施； 4、接触过模型量化、精度校准等技术，在课程实验或项目中实践过相关调优方法； 5、具备TensorRT/CUTLASS/Trition等工具的学习或使用经历，理解其性能加速原理。

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