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Open nowPosted 826 days ago

生成式推荐算法工程师（内容消费） - 内容消费与非闭环营销

ByteDance19,268 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now生成式推荐算法工程师（内容消费） - 内容消费与非闭环营销ByteDance · 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 826 days ago

ByteDance median: 9 days open

The posting

团队介绍：内容消费与非闭环营销技术团队，服务内容消费和非闭环营销行业数十万客户和十亿级用户。其中，内容消费行业包括游戏、小游戏、短剧、小说、社交与工具等行业，非闭环营销行业包括平台电商、引流电商等行业。内容消费与非闭环营销围绕客户深转、ROI等本质诉求，构建了完整的产品矩阵，知名产品有UBA/UBP自动化产品、深转ROI1\ROI7产品等。研发团队主要通过技术手段支持行业的收入和市占提升，工作内容包括：投放策略优化（出价、自动化）、模型优化（深度延迟和稀疏转化建模、序列建模、Large Recommend Model等）、流量和体验策略优化（ROI3产品、推荐广告融合、多目标建模等）、AI内容理解和生成（多模态模型、营销创意&剧集生成等）、定价和权益激励策略优化（智能定价、QCPX等）、海量创意自动化托管投放架构（DPA）、新产品开发等。

1、生成式推荐模型研发：负责基于LLM生成式模型的推荐策略设计与优化，探索生成式技术在用户兴趣建模、内容生成与匹配、个性化推荐中的创新应用，提升推荐的相关性、多样性与用户体验； 2、多模态融合与理解：深入研究并应用多模态技术，融合文本、图像、视频等信息，提升对内容和用户意图的理解能力，为生成式推荐提供坚实的表征基础； 3、系统实验与效果评估：参与生成式推荐系统的实验设计与效果评估，通过A/B测试等手段科学验证模型改进的有效性，并进行深度归因分析； 4、技术落地与业务协同：与产品、运营团队紧密协作，将生成式技术落地到实际推荐场景（如短视频、图文、直播），推动业务指标（如用户时长、GMV、广告收入）的显著提升； 5、前沿技术跟踪与创新：密切跟踪生成式AI与推荐系统领域的最新研究进展，引入创新方案解决业务痛点，持续探索技术边界。

1、计算机/人工智能/统计学等相关专业硕士学位及以上，或本科学历有3年以上生成式模型、推荐系统相关工作经验； 2、扎实的机器学习/深度学习理论基础，熟悉生成式模型（如Transformer、LLM、多模态生成模型、扩散模型等）的原理与应用； 3、熟悉至少一种主流深度学习框架（PyTorch/TensorFlow），具备较强的代码实现能力； 4、有扎实的编程基础，良好的编程风格和工作习惯，熟练掌握C/C++、Java、Python等至少一门语言； 5、有推荐系统或生成式模型（如LLM、AIGC）相关项目经验者优先； 6、具有良好的团队协作能力、沟通能力和问题解决能力，对新技术有强烈的探索欲望。

加分项 1、在广告、推荐、搜索、增长营销等业务上有过多年的优化经验； 2、在顶级会议（CCF-A类，如ICML、NIPS、ICLR、AAAI、IJCAI、KDD、RecSys、SIGIR等）上发表过论文； 3、有过深度学习编程框架开源社区经验。

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