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Open nowPosted 281 days ago

机器学习系统调度编排实习生-Data AML

ByteDance19,260 open roles

Where
Hangzhou, Zhejiang, Chinese Mainland
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Your applicationOpen now机器学习系统调度编排实习生-Data AMLByteDance · Hangzhou, Zhejiang, 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、分布式编排调度，解决服务/模型训练、推理任务的分布式部署： （a）使用/二次开发诸如Kubernetes，Yarn，Mesos，Celery等分布式调度框架，并可以在不同的业务场景下合理选型；依据各框架的特点进行集群的利用率/均匀性的调度策略优化； （b）对接/扩展各框架在水平/垂直扩展甚至AutoScaling的工作；参与多集群混合调度（类似K8S中Federation）的适配工作；负责不同优先级服务的抢占/驱逐功能；负责不同集群不同类型资源间的拆借/混部对接工作；负责多机房、多地域、多云场景的调度/负载适配； 2、资源撮合层，解决多角色之间的资源联合分配问题：从全局角度进行分配率和资源运营效率的优化；解决各种CPU/GPU/其他异构硬件/模型数据/样本数据/外部调用资源间的容量协调和联合匹配；感知拓扑限制，进行微拓扑优化，从而优化整体网络带宽使用；海量资源和多租户的预算/交付联动；保障性资源/预算外资源，以及混部/超卖的场景对接； 3、参与训练场景的流程/功能需求，诸如阶段性编排，批流阶段封装；提升训练单副本服务的稳定性，如Failover保护；更多的备份点策略；可观测性、可操作性、鲁棒性以及用户体验优化； 4、参与包括离线到在线同步，数据一致性和更新时效性优化；也包括在线服务多副本的异构资源/稳定性预案的流量调度；模型与服务的动态在线编排和集群间坐落编排。

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Go/Python至少1种编程语言，有良好的编程习惯及编码能力； 3、熟悉分布式系统原理，有机器学习系统相关实践和开发经验优先； 4、熟悉开源的分布式调度框架，诸如Kubernetes（K8S），Yarn（Flink，MapReduce），Mesos，Celery，对其中之一有实践积累者优先； 5、有强烈的工作责任心，较好的学习能力、沟通能力和自驱力，能够快速的响应和行动。

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