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Open nowPosted 281 days ago

机器学习存储架构实习生-Data AML

ByteDance19,260 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now机器学习存储架构实习生-Data AMLByteDance · Beijing, Chinese Mainland

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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 281 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：Data AML是字节跳动的机器学习中台，为抖音/今日头条/西瓜视频等业务提供推荐/广告/CV/语音/NLP的训练和推理系统。为公司内业务部门提供强大的机器学习算力，并在这些业务的问题上研究一些具有通用性和创新性的算法。同时，也通过火山引擎将一些机器学习/推荐系统的核心能力提供给外部企业客户。

1、通用性/架构设计取舍：如何构建一个统一的中台系统，但同时可以支持多个业务在不同场景(低成本/高可用性/高吞吐/高性能/大空间)的诉求； 2、多级/分级存储的架构复杂度：不仅限于显存/内存/外存； 3、跟进前沿的软件/硬件架构演进与尝试； 4、作为站内中台，多代际/多机房/多套餐/不同库存大量资源的规划与利用率优化；对于模型规模/服务流量感应后做出的自动/动态调优问题； 5、多个子系统的多种目标的优化：训练部分的功能性、可用性、容错性；serving部分的成本、容量、性能；系统同步部分的数据一致性、实效性、带宽容量； 6、针对一些索引/存储结构不断追求更优：诸如无锁化/渐进式数据结构的无限追求；对于CAP定理不断的挑战与追求。

1、2027届本科及以上学历在读，计算机、软件工程等相关专业优先； 2、熟练掌握Linux环境下的C++/Python编程语言的使用； 3、掌握分布式系统原理，参与过大规模分布式系统的设计、开发、维护和持续优化，能够识别复杂分布式系统中的潜在问题； 4、参与过推荐/搜索/机器学习等分布式系统工作，涉及过诸如资源调度、任务编排、模型训练、模型推理、特征抽取、MLSys、AIOps等内容； 5、有优秀的逻辑分析能力，能够对业务逻辑进行合理的抽象和拆分，具备良好的团队合作精神； 6、有强烈的工作责任心，较好的学习能力、沟通能力、自驱力和执行力，具备良好的工作文档习惯，及时按要求撰写更新工作流程及技术文档。

加分项 1、参与过类似ParameterServer系统优化，或者搜索系统的索引结构优化； 2、参与过KVCache类系统（如Mooncake等）开发或优化； 3、了解Redis、LevelDB/RocksDB、Mongo等开源存储类项目；或者有HDFS、Ceph等大型分布式存储系统使用/优化经历； 4、熟悉机器学习框架（TensorFlow/PyTorch/MXNet）； 5、有以下某一方向领域的经验：AI Infrastructure，HW/SW Co-Design，High Performance Computing，ML Hardware Architecture（GPU，Accelerators，Networking），Machine Learning Frameworks，ML for Systems，Distributed Storage。

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