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Open nowPosted 963 days ago

后端调度编排工程师 - Data AML

ByteDance19,268 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now后端调度编排工程师 - Data AMLByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.9% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 9 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.9%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 963 days ago

ByteDance median: 9 days open

The posting

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、负责分布式编排调度中的资源效能优化，通过工程手段，提升单位算力上支持的业务、模型规模： 1）使用/二次开发围绕Kubernetes/Godel生态的分布式调度框架，在不同的业务场景下合理选型，并依据不同场景的特点进行集群的利用率/均匀性的调度策略优化； 2）对接/扩展各类模型、业务的AutoScaling以及自动并行的工作；通过对不同模型的负载建模分析的方法，为模型的资源请求进行自动优化，规模化的优化资源使用效率，达到全局最优； 3）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、负责建设面向下一代超大超深推荐模型的训练系统架构： 1）围绕超大规模Embedding以及超大规模GPU同步训练建设弹性、鲁棒的分布式训练Runtime； 2）设计、优化面向未来推荐广告模型调研范式（e.g.RL、Finetune、蒸馏）的分布式计算API以及Runtime； 3）对接平台，优化分布式训练的可诊断性以及易用性； 3、负责建设面向下一代推荐系统的在线编排架构： 1）围绕超大规模Embedding的在线训练场景，构建健壮、稳定的分布式模型推理架构； 2）结合业务的调研实验模式，优化推荐广告模型在线架构的易用性和MLops流程。

1、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； 2、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； 3、熟悉开源的分布式调度框架，诸如Kubernetes（K8s），Yarn（Flink，MapReduce），Mesos，Celery，对其中至少一种有实践积累者优先； 4、熟悉开源的分布式编排系统，例如veRL，vLLM，Ray，TFX，对其中至少一种有开发经验者优先； 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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