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Open nowPosted 190 days ago

面向大模型与AI Agent的AI云原生基础设施关键技术研究-数据库（北京/深圳/杭州/成都）

ByteDance19,257 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now面向大模型与AI Agent的AI云原生基础设施关键技术研究-数据库（北京/深圳/杭州/成都）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 190 days ago

ByteDance median: 16 days open

The posting

团队介绍：字节跳动基础设施数据库团队，是支撑集团业务的核心技术底座之一。我们管理着业界超大规模的数据库集群，承载了抖音、豆包大模型、今日头条、电商、飞书等核心业务的万亿级流量。从海量并发的事务处理到极致复杂的实时分析，我们直面全球最严苛的技术挑战，同时通过火山引擎为众多企业客户提供具备企业级稳定性的全栈数据库服务。团队基于自研技术栈，打造了涵盖关系型数据库、NoSQL 数据库、多模数据库、大规模图计算平台、多模态检索、云原生中间件的完整数据库产品矩阵。我们践行 “务实浪漫” 的极客文化，不仅在 VLDB、SIGMOD、ICDE 等顶级会议持续输出突破性成果，更将极致的性能优化与软硬协同技术落地于实际生产环境，引领数据库技术迈向云原生新纪元。AI 时代，我们践行 “勇攀高峰”，正致力于构建 Agent-First 的新一代数据架构，打破模型与数据的边界，赋予数据库在智能体生态中主动感知、推理辅助与复杂记忆管理的核心能力。我们的研发团队分布于国内/海外多地，汇聚了行业顶尖的数据库专家，与全球顶尖高校、研究机构深度合作，潜心打磨数据库技术。现诚邀具备数据库内核研发经验、分布式系统架构能力及 AI 创新视野的顶尖极客，携手探索技术无人区，定义 AI 时代的数据基座，赋能全球企业实现 AI 驱动的业务变革。

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届毕业，获得博士学位，优先考虑人工智能、计算机科学、数学相关专业； 2、了解常用量化压缩算法（PQ、SQ、RabitQ等）、向量索引（IVF、HNSW、NSG、DiskANN、LSH等）与向量数据库（Milvus/Chroma/Pgvector等）的基本原理； 3、以第一作者或除导师外第一作者身份在SIGMOD/VLDB/OSDI/SOSP/ICML/ACL/ICLR等（或同等级别DB/系统/AI顶会）发表过论文，具备推进研究问题、设计实验、写作投稿的能力； 4、热爱编程，有较强的学习能力，有强烈的求知欲、好奇心和进取心，能及时关注和学习业界最新技术。

加分项： 1、在ACM-ICPC、NOI等信息学竞赛中获得区域金牌及以上奖项； 2、有经典数据库内核/优化器/执行引擎经验（PostgreSQL/SparkSQL/Neo4j等），或在索引设计、存储格式、事务/并发、Compaction等方面有实战经验； 3、在Agentic RAG、LLM Finetune（SFT/LoRA/RLHF等）、检索增强评测、对齐与可靠性等有实战经验； 4、熟悉PyTorch等框架，能完成训练/推理/评测管线搭建； 5、熟悉GPU架构与CUDA C++，或有向量计算/检索算子GPU化经验； 6、熟悉分布式系统基本原理（共识、复制、调度、资源隔离、可观测性等）及相关工程框架。

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