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Open nowPosted 71 days ago

推荐算法工程师 - 中国交易与广告

ByteDance19,258 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now推荐算法工程师 - 中国交易与广告ByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.4% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 23 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 71 days ago

ByteDance median: 23 days open

The posting

团队介绍：中国交易与广告，基于个性化推荐、深度学习和大模型技术，依托抖音、今日头条、红果短剧、西瓜视频、番茄小说等多元产品，构建抖音电商、生活服务、广告营销等核心营收业务的算法策略和工程架构。部门运用领先的算法能力，精准连接海量用户与商家，通过推荐、广告和搜索算法精准匹配需求并促进交易达成，保障用户体验与商家权益，实现营收业务可持续增长；基于深厚的技术积累，构建高效、智能、可靠的交易与广告产品，全面提升多行业收入变现与市场占有。在这里，你将直面超大规模流量与复杂业务场景的技术挑战，深度应用业界领先的商业模型与算法技术，投身面向未来的数字营销能力建设，持续探索前沿技术。

1、负责推荐系统链路的算法与模型迭代，面向“召回→粗排→精排→重排/混排”的完整流程，持续提升分发效率与用户体验，并对指标（点击、转化、完播/停留、交易价值与长期价值等）负责； 2、建设多路召回与向量检索体系：包括双塔/表征学习、ANN检索、语义召回与多模态召回等方向；结合用户意图、上下文与实时信号，提升覆盖率、相关性与实时性； 3、建设粗排、精排与重排/混排模型体系：以Transformer为核心进行序列建模与交互建模，结合样本/特征/损失设计与训练技巧，在稳定性、鲁棒性与在线收益之间做权衡；在用户体验、商业收益、生态健康与合规约束之间做平衡；结合因果推断、强化学习、约束优化与探索-利用机制，提升长期收益与系统稳定性； 4、推动模型Scaling与训练范式升级：围绕更大规模数据、更长序列、更大模型与更强训练策略（如自监督预训练、多任务学习、蒸馏、对比学习等）实现可控收益，并沉淀可复用的训练与评估方法论； 5、探索生成式推荐与LLM4Rec：将大模型的语义理解与推理能力引入候选生成、召回增强、重排、混排等环节，提升复杂需求理解、冷启动与长尾分发效果； 6、推进推荐工程化落地：构建样本与特征体系、离线训练与在线推理链路，优化时延与成本（量化/蒸馏/剪枝/推理加速、向量服务、特征一致性与可观测性），并通过AB实验与评测体系实现持续迭代闭环。

1、2027届获得本科及以上学历，计算机、数学、统计学、电子工程或相关专业优先； 2、具备扎实的数据结构、算法与机器学习/深度学习基础，对推荐/广告/搜索等方向至少一个有系统性理解； 3、熟悉主流深度学习框架（PyTorch/TensorFlow等）与常见训练/推理优化方法，具备良好的工程实现能力与代码习惯； 4、具备优秀的数据分析与问题拆解能力，能够从业务目标出发定义指标、定位问题、设计实验并推动上线迭代； 5、对Transformer、序列建模、Embedding/表征学习、向量检索、重排/混排与多目标优化等方向有实践经验者优先； 6、有大规模推荐系统、在线实验、高并发服务、特征工程或样本体系建设经验者优先。

加分项 1、有生成式推荐/LLM4Rec、语义召回、大模型重排、多模态理解或相关方向的落地经验； 2、在顶级会议（CCF-A类，如ICML、NeurIPS、ICLR、AAAI、IJCAI、KDD、RecSys、SIGIR等）发表过论文或有高质量开源贡献； 3、有金融/保险/电商/本地生活/线索经营等复杂交易场景经验，或对合规约束与长周期经营问题有深入理解。

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