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Open nowPosted 85 days ago

搜索大模型算法工程师 - TikTok Shop

ByteDance19,260 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now搜索大模型算法工程师 - TikTok ShopByteDance · Beijing, Chinese Mainland
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The clock on this job

Early applications get read.

11.3% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 27 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.3%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 85 days ago

ByteDance median: 27 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、大模型与Agentic Search技术研发:探索利用LLM增强用户意图理解,优化从召回到排序的全链路个性化体验,包括模型复杂化的Scaling Law、长序列建模、生成式搜索、Generator/Evaluator个性化混排、LLM Query理解、改写、生成、Agentic Search System探索等;参与下一代Agentic Search引擎研发,负责全链路(Query理解、规划、召回、重排、生成)的算法设计与优化,包括Query Understanding、Retrieval、Ranking、RAG、LLM、Agent、多模态、强化学习等方向,持续提升搜索相关性、回答质量、用户体验及业务价值;参与Agentic Search核心能力建设,负责Search Agent、Deep Search、Deep Research等能力研发,探索Planning、Tool Use、Multi-hop Retrieval、Information Grounding、Citation、Self-Verification等关键技术在真实搜索场景中的落地; 2、多模态搜索技术创新与应用:融合文本、图像、视频等多模态信息,构建统一的理解与表征模型、语义量化方案,探索多模态与效率模型Cotrain的技术创新; 3、搜索产品全链路设计与优化:深入参与搜索产品的需求设计,围绕商品、视频、直播等不同体裁从前端样式到引擎服务和算法设计,从新品冷启动、用户复购到供给建模,全链路探索多体裁、多目标的搜索系统建设,持续提升产品体验;与产品、工程团队共同推动算法方案高质量落地,持续优化Agentic Search产品体验,打造面向亿级用户的新一代智能搜索系统; 4、搜索基础模型与数据体系建设:参与搜索基础模型(Search Foundation Model)训练,构建覆盖数据、评测、训练的完整闭环,包括SFT、Preference Learning、RLHF/RLVR、Distillation、Synthetic Data、Self-Improvement、Long Context、Memory等方向,持续提升模型推理能力与泛化能力;参与Agentic Search数据体系建设,挖掘用户行为与搜索日志,构建Query理解、召回、排序、Agent推理等训练数据,设计Rubric、LLM-as-a-Judge、自动化评测体系,持续优化模型效果; 5、前沿技术探索与应用:持续关注国际前沿研究,探索大模型、Agent、多模态、强化学习、长上下文、Memory、Inference-time Compute等技术在Agentic Search场景中的创新应用。

1、2027届获得硕士及以上学位,人工智能、计算机、软件工程、电子信息、自动化、数学、统计等相关专业优先; 2、具备扎实的数据结构、算法和编程基础,熟练掌握C/C++、Python等至少一种编程语言,具有良好的工程实现能力; 3、熟悉机器学习、深度学习基础,了解Transformer、Attention、Embedding、Optimization等核心算法,熟悉PyTorch等深度学习框架; 4、在搜索、推荐、广告、信息检索(IR)、自然语言处理(NLP)、大语言模型(LLM)、多模态、强化学习(RL)等一个或多个方向具有较深入的研究或实践经验者优先; 5、具备良好的问题分析与解决能力,具有较强的学习能力、沟通协作能力和技术探索精神,能够快速推动创新技术落地。

加分项 1、具有扎实的机器学习、深度学习、强化学习基础,在Agentic Search、Search Agent、Deep Search、Deep Research、LLM、RAG、Post-training、多模态、World Model、RLHF/RLVR等方向具有深入研究经验; 2、在KDD、SIGIR、WWW、ICML、NeurIPS、ICLR、ACL、EMNLP、NAACL、CVPR、ECCV、ICCV、AAAI、IJCAI、CoRL等国际顶级会议或期刊发表论文者优先; 3、获得ACM/ICPC、NOI、IOI、TopCoder、Kaggle等国内外算法竞赛奖项者优先; 4、具有Agentic Search、Search Agent、Deep Search、Deep Research、RAG、LLM Post-training、Preference Learning、RLHF/RLVR、Synthetic Data、Long Context、Memory等方向的项目经验者优先; 5、具有大规模搜索引擎、推荐系统、计算广告、分布式训练、大模型训练与推理优化、超大规模数据计算等相关经验者优先; 6、主导或参与过具有较大影响力的Agentic Search、大模型、Agent或AI应用项目,具有优秀的技术创新能力和工程落地能力者优先。

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