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Open nowPosted 85 days ago

推荐算法工程师 - TikTok Shop

ByteDance19,258 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now推荐算法工程师 - TikTok ShopByteDance · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

15.1% 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. 2.4%1 day
  2. 6.5%3 days
  3. 15.1%7 days
This job: posted 85 days ago

ByteDance median: 23 days open

The posting

团队介绍：TikTok Shop 是 TikTok 旗下的内容电商业务，致力于成为用户发现并获取优价好物的首选平台。面向直播电商、视频内容电商、货架电商等多元购物场景，国际电商持续为全球用户提供更个性化、更主动、更高效的消费体验，也为商家提供稳定、可靠、可持续增长的平台服务，推动“新奇好物畅销全球，美好生活触手可得”的使命落地。 目前，国际电商算法团队分布在中国、美国、新加坡等多个国家和地区，深度参与全球化业务建设。加入我们，你将有机会面向全球用户、商家和商品生态，与跨区域的产品、工程、运营和业务团队协作，持续提升商品供给质量、用户购物体验、平台交易效率与履约体验，共同探索内容电商与智能购物的新模式。 Data-国际电商团队是 TikTok Shop 的核心算法技术力量，专注于电商场景下的算法创新与规模化落地，方向包括但不限于：生成式推荐与大规模 Foundation Model、多模态商品理解与内容生成、搜索与导购、智能客服、风控治理、物流履约优化、商家经营智能化，以及 Agentic 智能体在电商全链路中的应用。我们希望通过先进的算法能力，帮助用户更高效地发现感兴趣的商品，帮助商家实现更高质量的经营增长，并持续提升交易各环节的智能化水平。 在这里，你将与一流的产品和技术团队合作，面对复杂且真实的国际化电商挑战，推动前沿技术在大规模业务场景中落地，与团队一起创造更多可能。

1、负责推荐系统全链路的搭建和优化：参与商品、短视频、直播多场景的个性化推荐算法研发，覆盖召回、粗排、精排、重排混排、供给和生态优化、用户增长、体验复购、兴趣探索等推荐系统各种环节的算法优化工作； 2、用户兴趣建模： 1）超长行为序列建模：在万级长度的用户行为序列上做兴趣建模，处理位置编码外推、注意力复杂度、在线推理时延与存储成本之间的真实权衡； 2）长短期兴趣解耦：区分稳定偏好、季节性需求、即时冲动与已被满足需求，优化用户的长短期兴趣表征和抽取； 3）隐式负反馈与潜在兴趣：从曝光未点、连续跳过、快速划走等弱信号中挖掘信息，同时通过探索机制发现用户自己尚未表达的兴趣；研究隐式负反馈在推荐系统各个环节中的应用，以及如何解决推荐系统feedback loop的问题； 4）多模态与表征学习：结合商品图像、文本、视频内容，以及丰富的用户行为数据，构建高质量的物品和用户表征； 3、冷启动与内容生态：负责内容生态的机制设计；新商品、新主播、新创作者每天大量涌入，需设计高效的探索和扶持机制，在有限流量下快速判断潜力；构建商品与主播的质量、潜力、专业度、口碑信号（如复购率模型、批量化低质内容识别），让优质供给获得更多曝光，提升作者留存；除了人找货，系统的设计“货找人”的推荐链路，研究和设计实现双边匹配的推荐系统； 4、参与长期价值建模、user复购/留存建模、成交归因、疲劳度建模、新用户推荐、增量价值建模、LLM4rec等真实推荐系统中各种前沿和不确定性的话题，业界没有标准答案，我们期望你在这些有挑战的问题上做出原创性的工作； 5、用Agent重写研发范式：我们正在把Coding Agent深度嵌入算法研发的各个流程，希望你能与我们一起高效重构超大规模推荐系统和模型的开发、设计、评估、以及迭代和实验流程。

1、2027届获得本科及以上学历，人工智能、计算机、数学、统计等相关专业优先； 2、扎实的算法与数据结构基础，优秀的编码能力，能写出干净、高效、可复现的代码； 3、扎实的机器学习与概率统计基础：理解模型背后的假设与失效边界，能讲清楚一个方法为什么有效、在什么条件下会失效，而不止于调用API； 4、熟悉深度学习框架（PyTorch/TensorFlow）与至少一种大规模数据处理工具（Spark/Flink/SQL），熟悉Python，具备C++与Linux开发环境的基本能力； 5、具备数据敏感度与实验素养：能从指标波动中定位真实原因，理解A/B实验的设计、置信度与常见陷阱； 6、优秀的学习能力、清晰的表达能力与良好的协作精神。

加分项： 1、有推荐、搜索、广告、信息检索、NLP或大规模机器学习系统的研究或工程实践经验； 2、在KDD、NeurIPS、WWW、SIGIR、WSDM、ICML、ICLR、RecSys、CIKM等会议发表过论文，或有高质量开源贡献； 3、有Kaggle、天池、RecSys Challenge等竞赛获奖经历，或ACM-ICPC/NOI竞赛背景； 4、有因果推断、在线学习/Bandit、图神经网络、序列建模或大规模分布式训练的实践经验； 5、AI coding及AI自动化搭建系统、优化模型的重度用户。

为什么选择我们 1、工业级推荐系统：十亿级用户、亿级商品、日均千亿级行为样本；很多在论文里被简化掉的问题，在这里是每天要面对的现实； 2、完整的闭环推荐链路：从数据、特征、模型到线上实验与业务收益，你能看到自己的想法在多长时间内、以多大幅度改变了真实世界的交易； 3、Open开放的团队氛围：在这里，你有机会和优秀的算法工程师共事，只要有想法，就会有无限种可能和创意的碰撞、探索； 4、技术与业务不割裂：我们要求算法工程师理解业务，也保护算法工程师做长期技术投入的空间；这里衡量你的是判断力和影响力，不是工时； 5、站在高速增长业务的最前沿：TikTok Shop正加速在全球各地区落地，你有机会亲历从冷启动到规模化爆发的完整周期；背靠TikTok庞大且仍在快速扩张的用户基础，电商业务的增长天花板远未触及。

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