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Open nowPosted 43 days ago

机器学习平台产品Leader - Data AML

ByteDance19,268 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now机器学习平台产品Leader - 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 43 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AML Engine机器学习平台的产品规划与建设，连接业务、算法、基础设施研发和Production Engineer团队，围绕异构算力、编排调度、机器学习框架和生产管理持续完善平台能力，在支持业务快速探索的同时，提升系统稳定性、资源效率和研发协作效率； 2、负责机器学习平台的产品战略、路线图和需求优先级，结合推荐、广告等业务目标，识别平台需要解决的关键问题，推动产品从规划、设计到上线运营的完整闭环； 3、负责产品能力建设与架构规划： 1）深入机器学习开发、训练、评估、部署、推理和生产治理流程，理解算法工程师及业务团队的真实痛点，将零散需求抽象为标准化、可复用的平台能力； 2）推动GPU、AI加速卡等异构算力的产品化接入，围绕兼容适配、业务迁移、灰度验证、故障回退、可观测性和默认配置设计完整体验，降低新硬件使用门槛； 3）规划编排调度产品能力，覆盖资源申请、配额、弹性、隔离、任务调度和生命周期治理，兼顾标准场景的SaaS化体验与探索场景有边界的PaaS化扩展； 4）与框架和算法团队共同定义产品需求与验收标准，按照“正确性、分布式与容错、规模能力、性能”的优先级推动框架能力建设； 4、负责大规模机器学习系统生产管理产品建设： 1）建设面向大规模机器学习系统的生产管理产品，包括可观测性、风险预警、负责人绑定、升级机制、自助诊断、资源治理和自动化闭环，推动生产问题从人工响应转向平台化和智能化治理； 2）对重复扩容、资源利用率低、负载不均、资源碎片及反复人工支持等问题，推动形成调度、平台、自动化或治理层面的源头解，而非停留在单次需求承接； 3）明确业务、研发与Production Engineer的协作边界：业务负责模型生命周期和风险决策，平台提供监控、预警、护栏、自助工具及可审计的闭环机制； 5、建立产品指标体系，持续跟踪平台采用率、任务交付效率、事故与风险闭环率、资源利用率、单位资源成本、自助率、自动化率和重复人工需求下降情况； 6、协同算法、研发、PE、资源及业务团队推进复杂项目，明确决策机制、里程碑、风险门槛和验收标准，平衡业务增长、生产稳定性、资源成本与长期平台建设。

1、本科及以上学历，计算机、人工智能、数据科学、统计学或相关专业优先，3年及以上平台/技术产品经理工作经验； 2、有机器学习平台、数据平台、云原生平台、开发者工具等相关经历； 3、理解机器学习系统的基本链路，能够与算法、调度、框架及基础设施研发团队有效沟通，不要求承担底层系统研发工作； 4、具备较强的需求抽象和平台化思维，能够从单点业务诉求中识别共性问题，形成边界清晰、可复用、可规模化的产品方案； 5、具备良好的能力素养： 1）具备良好的数据分析能力，能够建立指标、识别基线并通过量化结果判断产品价值； 2）具备复杂项目推动能力，能够协调多个上下游团队，推动关键决策、风险暴露和结果闭环； 3）对生产稳定性有足够敬畏，能够在业务速度、系统风险、资源成本和用户体验之间做出合理取舍； 4）具备良好的产品文档和沟通能力，能够清晰表达产品目标、能力边界、优先级及验收标准。

加分项 1、有推荐、广告、搜索或机器学习基础设施产品经验； 2、熟悉GPU、AI加速卡等异构计算资源的使用和管理场景； 3、了解Kubernetes、资源调度、分布式训练、在线推理或主流机器学习框架； 4、有MLOps、模型生命周期管理、资源利用率治理、稳定性平台或开发者体验产品经验； 5、具备SQL、数据分析或基础技术原型能力； 6、有通过自动化、AI Agent、CLI或自助工具减少人工操作的产品实践。

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