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Open nowPosted 191 days ago

【实习】面向大模型与AI Agent的AI云原生基础设施关键技术研究-网络（北京//杭州）

ByteDance19,207 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now【实习】面向大模型与AI Agent的AI云原生基础设施关键技术研究-网络（北京//杭州）ByteDance · Beijing, Chinese Mainland

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  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 191 days ago

ByteDance median: 2 days open

The posting

团队介绍：字节跳动基础架构网络团队，负责集团全球网络基础设施设计、研发及运营工作，包括全球骨干网、数据中心网络、AI高速网络、云网络、容器网络、四七层应用网络等，基于先进的交换芯片和光互连技术，我们通过自研交换机、光芯片打造高性能的数据中心网络；基于自研RDMA协议、拥塞算法、集合通信库的创新，我们打造了超大规模的AI高速网络；基于全自研的SDN、NFV、端到端的QUIC协议、拥塞算法以及软硬结合HPA，我们设计了拥抱时代的云计算网络产品体系以及数据中心高性能的四七层网关平台、DNS系统、流量加速平台等；基于字节人工智能的加持，我们以Autopilot为目标设计了一套支撑全球网络平台的IBN、流量工程、网络观测平台。如果你对以上技术感兴趣，愿意解决超大规模的网络问题，欢迎加入我们。

课题介绍： 随着大语言模型与AI Agent规模化落地，传统云原生基础设施已难以适配AI负载的极致性能与弹性需求。本课题围绕AI基础设施全栈展开系统性研究： 1、网络与可观测：研究大规模AI集群故障智能定位与根因分析，结合时序数据库智能调优，提升集群稳定性； 2、存储系统：研发AI场景专属的Serverless高性能弹性文件系统与存储加速架构，探索DPU软硬件协同优化，突破AI存储性能瓶颈； 3、算力调度：研究GPU/CPU/MEM异构协同调度技术，面向AI Agent构建Serverless异构算力编排系统，解决负载异构、状态依赖等调度难题； 4、向量检索：优化面向大模型应用的向量检索核心技术，打造云原生分布式向量索引引擎，满足超大规模向量检索的低延迟、低成本需求； 5、智能化与Agent架构：探索基于AI Agent工作流的基础设施自动寻优，构建可自主进化的业务Agent框架，通过AI for Infra赋能全栈智能优化； 本课题旨在构建支撑大模型与AI Agent落地的下一代AI原生基础设施，提升资源利用率、降低成本、支撑弹性扩展，推动AI基础设施技术演进。

课题挑战： 1、全栈协同挑战：覆盖多技术领域，需要从端到端视角实现系统性优化，避免单点优化收益不足； 2、性能成本平衡：AI场景对性能提出极致要求，需要在吞吐、延迟、规模与成本之间找到最优平衡点； 3、云原生适配：需要将传统单机技术方案重构为适配云原生分布式架构，解决扩缩容、容错、调度等新问题； 4、AI系统融合：既需要用AI赋能基础设施优化，又需要基础设施原生适配AI负载，深度融合对技术整合要求高； 5、Agent稳定性：自主进化Agent框架需要解决经验学习、知识一致性、持续安全进化等基础问题，工程化难度大； 6、研发生态平衡：需要平衡前沿理论创新与工程落地，兼顾技术突破与现有业务生态适配。

课题价值： 1、构建大模型/RAG 场景的高性能基础设施底座，支撑大规模向量数据高效检索； 2、优化AI业务存储成本结构与运维复杂度； 3、提升异构算力资源配置效率与技术复用性。

1、2027届及以后毕业，博士在读，网络、通信和计算机等相关专业； 2、拥有扎实的计算机网络等相关领域理论基础，精通计算机网络和网络编程，熟练掌握至少一门主流编程语言，如C/C++、Python、Go等，具备优秀的代码能力、数据结构和基础算法功底； 3、针对不同研究方向，需具备相应能力，如能够独立完成复杂搜索任务的设计与开发；熟悉当前高性能网络和系统领域的最新进展，包括RDMA，拥塞控制、AI网络优化等技术； 4、具备高效的学习能力，拥有较强的自我驱动意识；具有良好的团队合作精神，善于与团队成员协作交流，共同攻克难题；出色的问题分析和解决能力，有自主探索解决方案的能力；出色的心理素质与应变能力，面对困难勇于迎接挑战，面对复杂局面沉着、冷静、灵活。

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