字节跳动Recommendation System Architecture Engineer | 推荐系统架构工程师- 筋斗云人才计划

Team Introduction: Data AML is ByteDance's machine learning middle platform, providing training and inference systems for recommendation, advertising, CV (computer vision), speech, and NLP (natural language processing) across businesses such as Douyin, Toutiao, and Xigua Video. AML provides powerful machine learning computing capabilities to internal business units and conducts research on general and innovative algorithms to solve key business challenges. Additionally, through Volcano Engine, it delivers core machine learning and recommendation system capabilities to external enterprise clients. Beyond business applications, AML is also engaged in cutting-edge research in areas such as AI for Science and scientific computing. Research Project Introduction: Large-scale recommendation systems are being increasingly applied to short video, text community, image and other products, and the role of modal information in recommendation systems has become more prominent. ByteDance's practice has found that modal information can serve as a generalization feature to support business scenarios such as recommendation, and the research on end-to-end ultra-large-scale multimodal recommendation systems has enormous potential. It is expected to further explore directions such as multimodal cotraining, 7B/13B large-scale parameter models, and longer sequence end-to-end based on algorithm-engineering CoDesign. Engineering research directions include: Representation of multimodal samples Construction of high-performance multimodal inference engines based on the PyTorch framework Development of high-performance multimodal training frameworks Application of heterogeneous hardware in multimodal recommendation systems 1. Algorithmic research directions include: 2. Design of reasonable recommendation-advertising and multimodal cotraining architectures 3. Sparse Mixture of Experts (Sparse MOE) 4. Memory Network 5. Hybrid precision techniques 团队介绍： Data AML是字节跳动公司的机器学习中台，为抖音/今日头条/西瓜视频等业务提供推荐/广告/CV/语音/NLP的训练和推理系统。为公司内业务部门提供强大的机器学习算力，并在这些业务的问题上研究一些具有通用性和创新性的算法。同时，也通过火山引擎将一些机器学习/推荐系统的核心能力提供给外部企业客户。此外，AML还在AI for Science，科学计算等领域做一些前沿研究。 课题介绍： 大规模推荐系统正在越来越多的应用到短视频、文本社区、图像等产品上，模态信息在推荐系统中的作用也越来越大。 字节实践中发现模态信息能够很好的作为泛化特征支持推荐等业务场景，端到端的超大规模多模态推荐系统的研究具有非常大的想象空间。 期望在算法和工程CoDesign基础上，对多模态Cotrain、7B/13B大规模参数模型、更长序列端到端等方向进一步进行探索。 工程上研究方向包括多模态样本的表征、基于 pytorch 框架的高性能多模态推理引擎、高性能多模态训练框架的构建、异构硬件在多模态推荐系统上的应用；算法上的研究方向包括设计合理的推荐广告和多模态Cotrain结构、Sparse MOE、Memory Network、混合精度等。 1、负责机器学习系统架构的设计开发，以及系统性能调优； 2、负责解决系统高并发、高可靠性、高可扩展性等技术难关； 3、覆盖机器学习系统多个子方向领域的工作，包括：资源调度、任务编排、模型训练、模型推理、模型管理、数据集管理、工作流编排、ML for System等； 4、负责机器学习系统前瞻技术的调研和引入，比如：最新硬件架构、异构计算系统、GPU优化技术的引入落地； 5、研究基于机器学习方法，实现对集群/服务资源使用情况的分析和优化。

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。

Team Introduction Our E-commerce is a content-driven commerce business built on globally-oriented short video platforms. Our mission is to become the go-to platform for users to discover and access high-quality products at great prices. Through multiple scenarios such as livestream e-commerce and video e-commerce, we aim to deliver a more personalized, proactive, and efficient shopping experience for users, while offering merchants a reliable platform to grow their business. We are committed to making great-value products easy to sell and easy to find across more regions, bringing a better life within reach for everyone. We invite you to grow with us, explore, innovate, and unlock your full potential as we tackle both technical and business challenges together. Our team brings rich experience in international product development, embraces diverse cultures, and operates R&D teams across the globe. Join us in facing the exciting challenges of cross-border collaboration, with opportunities for business travel and international assignments waiting for you! 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 ： 国际电商是以国际化短视频产品为载体的内容电商业务，致力于成为用户发现并获取优价好物的首选平台，在直播电商、视频内容电商等多场景下，国际电商业务希望能为用户提供更个性化、更主动、更高效的消费体验，为商家提供稳定可靠的平台服务，在更多的地区实现没有难卖的优价好物，让美好生活触手可得的使命。我们邀请你来此成长、钻研，发掘无限的潜力，一起应对技术和业务上的挑战。目前团队拥有丰富的国际化产品研发经验，包容多元的文化，且在全球设立研发团队，邀请你来一起接受跨国合作的挑战，还有出差外派机会在等你！ 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。

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: With the advancement of hardware computing and the continuous breakthroughs of large models in CV/NLP/multimodal learning and even AGI fields, the large computing driven in recommendation scenarios are increasingly capable of capturing user preferences in a more comprehensive and nuanced way. This enables a deeper understanding of user needs and the discovery of latent interests, ultimately leading to enhanced user experiences. As a critical component of short video recommendation systems, the ranking module is responsible for fine-grained matching between users and content, selecting the videos users are most likely to be engaged with. In this context, the key research focus is how to best leverage large computing to maximize the model’s memory, generalization, and reasoning capabilities. 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： 随着硬件算力的发展以及大模型在CV/NLP/多模态以至于AGI领域的不断突破，推荐场景下的大算力驱动能够帮助模型更全面深刻理解用户偏好，进而更好地理解用户需求，挖掘用户潜在兴趣，进而带来更好地用户体验。排序模块作为整个短视频推荐系统中非常重要的一环，承载着用户与视频之间的细粒度匹配挖掘进而挑选出用户最感兴趣的视频。如何找到合适的路径来最大化大算力下模型的记忆、泛化、推理能力，成为了研究的重中之重。