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Open nowPosted 190 days ago

飞书办公场景的企业级AI Agent智能体平台构建（Memory） - Seed 产品研发

ByteDance19,257 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now飞书办公场景的企业级AI Agent智能体平台构建（Memory） - Seed 产品研发ByteDance · Shanghai, 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：产品研发和工程架构-Seed 团队深耕大模型技术在场景中的创新应用，负责多款AI产品的工程研发 —— 包括豆包、飞书、豆包工作、豆包爱学、Gauth、猫箱等创新产品，覆盖消费级与企业级多个场景。我们致力于将大模型能力从 "生成内容" 推进到 "完成工作"，让 AI 服务用户日常与企业协作。你将面对多模态交互、工具调用可靠性、企业级数据安全等真实工程挑战，与团队一起推动 AI 生产力落地。

课题介绍： 随着大模型能力趋于成熟，飞书正处于将AI能力从单点功能升级为端到端“企业智能体平台”的关键窗口期。本课题聚焦于办公生产力场景，以Memory与Context体系为底座，以知识问答、会议数字分身为典型Long-Horizon任务场景，以自动化评测为质量度量引擎，以通用智能体框架为演进方向，系统性地探索企业级AI Agent的落地路径。目标是打造一套可扩展、可度量、可持续进化的办公智能体基础设施，让AI真正成为企业员工的协作伙伴与数字分身。

课题挑战： 1、Memory质量与演化：企业多源异构数据理解难度大，Memory的生产、消费与持续进化链路尚不成熟；现有学术Benchmark与真实办公场景存在较大差距，难以有效指导工程实践； 2、全模态流式内容理解：会议场景要求对音视频、文档、参会人上下文进行实时全模态理解，Streaming Omni能力的稳定性与泛化性是核心技术瓶颈； 3、评测体系的区分度与规模化：如何从海量线上Query中动态筛选出具有能力区分度的测试样本，并通过自动化手段生成覆盖全面、可校验的评分标准（Rubrics），以支撑天级别迭代，是工程与算法的双重挑战； 4、通用Agent框架的动态适配：Agent框架与多业务场景的动态适配、跨场景排序因子融合、奖励工程的训练数据稀缺，以及Memory的结构化与跨任务迁移，均是企业级智能体规模化落地的关键障碍。

课题价值： 1、产品价值：以飞书套件为依托，打通从Memory建设到Agent执行再到效果评测的完整闭环，实现AI能力从孤立Feature向协作式智能体平台的跃迁，显著提升企业用户的生产力效率； 2、技术引领：在Memory Benchmark、Omni Streaming 理解、LLM-as-a-Judge裁判模型、长程Agent任务等前沿方向建立技术积累，形成学术与工业界双向影响力； 3、生态战略：会议、文档、知识问答等场景天然是多Agent协作的孵化土壤，本课题构建的基础能力将为未来多智能体企业协作生态奠定核心基础，支撑飞书成为国内领先的企业 AI 智能体平台。

1、2027届毕业，获得博士学位，计算机科学、人工智能、信息检索、知识图谱等相关专业优先； 2、具备扎实的机器学习理论基础，对RAG、长上下文建模、记忆增强型Agent、知识存储与检索等领域有理论积累和强烈的研究兴趣； 3、具备良好的机器学习实践能力，有Agent记忆模块开发经验或者Memory评测经验优先； 4、创新思维突出，能从底层原理出发思考Context压缩、Memory管理、长期记忆持久化等核心问题，具备自主设计并验证解决方案的能力； 5、具备优秀的学习与分析能力、沟通与团队协作能力，能独立推进研究课题并在团队中有效输出成果。

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