字节跳动广告算法工程师-电商广告（Ranking方向）

1、负责字节跳动搜索广告算法优化工作，包括召回、粗排、精排、混排等整个ranking漏斗的模型优化； 2、利用NLP等技术进行多模态、图文内容理解、直播内容理解等建模优化，应用于整个搜广ranking链路，提高变现效率； 3、对深度学习算法、图神经网络算法和强化学习算法具备深厚功底，利用大规模机器学习算法行建模优化，提升转化效率； 4、针对搜索广告场景进行针对性特征建设和建模优化，提高搜广变现效率。

1、负责字节跳动搜索广告算法优化工作，探索LLM和Ranking相结合，在生成式召回、LRM等方向深入探索，提高Ranking全漏斗的变现效率； 2、利用NLP等技术进行多模态、图文内容理解、直播内容理解等建模优化，应用于整个搜广Ranking链路，提高变现效率； 3、对深度学习算法、图神经网络算法和强化学习算法具备深厚功底，利用机器学习算法行建模优化，提升转化效率； 4、针对搜索广告场景进行针对性特征建设和建模优化，提高搜广变现效率。

Team Introduction: TikTok is a global short-video platform available in 150 countries and regions. Our mission is to inspire creativity and bring joy by helping users discover real and interesting moments that make life better. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo. TikTok Research & Development (R&D) Team: The TikTok R&D team is dedicated to building and maintaining industry-leading products that drive the success of TikTok’s global business. By joining us, you'll work on core scenarios such as user growth, social features, live streaming, e-commerce consumer side, content creation, and content consumption, helping our products scale rapidly across global markets. You'll also face deep technical challenges in areas like service architecture and infrastructure engineering, ensuring our systems operate with high quality, efficiency, and security. Meanwhile, our team also provides comprehensive technical solutions across diverse business needs, continuously optimizing product metrics and improving user experience. Here, you'll collaborate with leading experts in exploring cutting-edge technologies and pushing the boundaries of what's possible. Every line of your code will serve hundreds of millions of users. Our team is professional and goal-oriented, with an egalitarian and easy-going collaborative environment. Research Project Introduction: As the world's leading short-video platform, TikTok faces multiple challenges in its recommendation systems, including data sparsity for new users leading to insufficient personalisation, high timeliness requirements for live steaming recommendations, difficulty in maintaining user interest diversity, and complex e-commerce recommendation system chains. Traditional recommendation methods heavily rely on historical behaviour modeling, which struggles with the cold-start problem for new users. Live-streaming recommendations demand real-time responsiveness to rapidly changing content dynamics (e.g., host interactions, traffic fluctuations) within extremely short time windows (typically within 30 minutes) posing higher demands on the system's real-time perception and decision-making capabilities. Additionally, the immersive single-feed format amplifies the challenge of maintaining content diversity, requiring a careful balance between multi-interest learning and the risk of content drift caused by exploratory recommendations. The current e-commerce recommendation system follows a multi-stage funnel architecture (recall–ranking–re-ranking), which often leads to inconsistent chains, high maintenance costs, and an overreliance on short-term value prediction. This leads users to fall into content homogenization fatigue. To address these pain points, this project proposes leveraging large language models (LLMs) and large model technologies to achieve significant breakthroughs. On one hand, LLMs—with their vast knowledge base and few-shot reasoning capabilities—can infer new users' potential intentions from registration data and external knowledge, thereby alleviating cold-start issues. On the other hand, by integrating graph neural networks (GNNs) and full-lifecycle user behavior sequences for modeling social preferences, we aim to improve the accuracy of interest prediction. Additionally, the project explores the generalization capabilities, long-context awareness, and end-to-end modeling strengths of large models to simplify the e-commerce recommendation chains, enhance adaptability to real-time changes, and improve exploratory recommendation effectiveness. The ultimate goal is to build a more streamlined system with more accurate recommendations, enhancing user experience and retention while driving sustainable business growth. 团队介绍 ： TikTok是一个覆盖150个国家和地区的国际短视频平台，我们希望通过TikTok发现真实、有趣的瞬间，让生活更美好。TikTok 在全球各地设有办公室，全球总部位于洛杉矶和新加坡，办公地点还包括纽约、伦敦、都柏林、巴黎、柏林、迪拜、雅加达、首尔和东京等多个城市。 TikTok研发团队，旨在实现TikTok业务的研发工作，搭建及维护业界领先的产品。加入我们，你能接触到包括用户增长、社交、直播、电商C端、内容创造、内容消费等核心业务场景，支持产品在全球赛道上高速发展；也能接触到包括服务架构、基础技术等方向上的技术挑战，保障业务持续高质量、高效率、且安全地为用户服务；同时还能为不同业务场景提供全面的技术解决方案，优化各项产品指标及用户体验。 在这里， 有大牛带队与大家一同不断探索前沿， 突破想象空间。 在这里，你的每一行代码都将服务亿万用户。在这里，团队专业且纯粹，合作氛围平等且轻松。 课题介绍： TikTok作为全球领先的短视频平台，面临新用户数据稀疏导致的个性化推荐不足、直播推荐时效性要求高、用户兴趣多样性维护困难以及电商推荐系统链路复杂等多重挑战。传统推荐方法依赖历史行为建模，难以解决新用户冷启动问题，且直播推荐需在极短窗口期内（通常30分钟内）实时捕捉内容动态变化（如主播互动、流量波动），这对系统的实时感知与快速决策能力提出更高要求。此外，单列沉浸式场景放大了多样性问题，需平衡多峰兴趣学习与探索引发的内容穿越风险。当前电商推荐系统采用多阶段漏斗架构（召回-排序-混排），存在链路不一致、维护成本高、过度依赖短期价值预测等问题，导致用户易陷入内容同质化疲劳。 针对上述痛点，项目提出结合大语言模型（LLM）和大模型技术实现突破：一方面利用LLM的海量知识储备与Few-shot推理能力，通过注册信息与外部知识推理新用户潜在意图，缓解冷启动问题；另一方面，在社交偏好建模中融合GNN与用户全生命周期行为序列，提升兴趣预测精准度。同时，探索大模型的泛化能力、长上下文感知及端到端建模优势，简化电商推荐链路，增强实时动态适应性与兴趣探索能力，最终实现系统更简洁、推荐更精准、用户体验与留存双提升的目标，推动业务可持续增长。

团队介绍：国际化电商广告团队始终以广告主为核心，追求技术卓越。我们与电商、零售、旅游等各行业广告主紧密合作，致力于将国际化产品打造为各类企业不可或缺的增长平台，同时成为消费者发现并购买产品、内容及服务的首选目的地。团队肩负着实现国际化广告营收的重要使命，正在全力构建新一代网络广告解决方案。 1、参与国际化广告系统的建设和迭代，持续改进电商广告的投放效果和全球数亿用户的体验； 2、运用机器学习、深度学习、LLM等技术，迭代广告Ranking链路和模型，提升流量变现效率； 3、运用运筹优化、因果推断等技术，优化广告Bidding系统，提高广告主预算使用效率； 4、结合AIGC、强化学习等技术，建设新一代自动化投放系统，同时优化广告投放的效率和效果； 5、设计流量策略和混排系统，全局优化广告、电商和自然内容的流量分配，平衡各业务目标和用户体验； 6、针对行业头部客户，设计定制化的效果优化方案，提升广告主预算增长。