The posting
团队介绍：Data AML是字节跳动的机器学习中台，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。
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，期望进一步通过算法与工程Co-design，探索推荐算法和大语言模型/多模态理解的深度融合主要研究方向包括：LLM4Rec、跨端跨场景的Foundation Model、千亿级参数规模的模型 和 多模态大模型。工程上研究方向包括多模态样本的高效表征、基于PyTorch架构的高性能推理引擎与训练框架构建，以及异构硬件在推荐场景下的性能极限优化；算法侧研究方向包括LLM4Rec技术路径的设计、千亿级底座模型的研发以及多模态协同训练（Co-training）等核心课题，旨在通过技术创新定义下一代工业级推荐范式。
课题挑战 1、千亿模型的训练和推理优化； 2、多模态表征统一与高效融合难度大； 3、Foundation Model的设计和工程优化； 4、算法工程协同设计与异构硬件适配复杂。
课题价值 1、技术价值：突破多模态表征融合、超大规模模型训练推理瓶颈，完善算法与工程协同设计体系，推动异构硬件适配与高性能框架国产化落地； 2、业务价值：提升推荐精准度与泛化能力，突破现有推荐系统模态局限，赋能短视频、文本社区等多产品，降低算力成本，助力业务规模化增长。
Topic Content In today’s global e-commerce landscape, intelligent systems must operate across increasingly complex and dynamic business environments. Yet existing approaches still face limitations in long-horizon forecasting, cross-Large-scale recommendation systems are being increasingly adopted across products such as short-video, text-based multimodal, and image platforms, with modality-specific in tremendous laying an ever-growing role in recommendations. Leveraging our latest research breakthroughs and broad industry insights, we believe modality information serves effectively as generalizable features to support recommendation and other business scenarios. Research on ultra-large-scale multimodal recommendation systems holds significant potential.
Topic Challenges 1.High difficulty in unifying multimodal representations and achieving efficient fusion; 2.Extremely high computational costs for training and inference of ultra-large-parameter models; 3.Difficulty balancing stability and efficiency in end-to-end modeling with long sequences; 4.Significant complexity in algorithm-engineering co-design and heterogeneous hardware adaptation.
Topic Value 1.Technical value: Achieve breakthroughs in multimodal representation fusion and training/inference bottlenecks for ultra-large-scale models; refine the co-design framework for algorithms and engineering; advance heterogeneous hardware adaptation and the development and deployment of domestically developed high-performance frameworks; 2.Business value: Enhance recommendation accuracy and generalization capability in multimodal scenarios; overcome the modality limitations of existing recommendation systems; empower multiple products including short-video and text-based community platforms; reduce computational costs; and drive scalable business growth.
1、2027届毕业，获得博士学位，人工智能、计算机科学、计算机工程或相关专业； 2、熟练掌握Linux环境下C/C++/Go/Python/Java等至少一种编程语言； 3、深入理解分布式系统原理，具备大型分布式系统的设计、开发与维护经验。
加分项： 1、熟悉Kubernetes架构，拥有丰富的云原生系统开发经验； 2、具备至少一种主流机器学习框架使用经验（如TensorFlow、PyTorch、MXNet）； 3、熟悉Django、Flask等相关技术，拥有后端开发工作经验； 4、具备以下任一领域相关经验：人工智能基础设施、软硬件协同设计、高性能计算、机器学习硬件架构（显卡、加速器、网络）、机器学习框架、系统机器学习、分布式存储。
1.Individuals who are completing or recently completed a PhD in Artificial Intelligence, Computer Science, Computer Engineering, or a related technical discipline; 2.Proficiency in 1 or more programming languages such as C/C++/Go/Python/Java in a Linux environment; 3.Deep understanding of distributed system principles, with experience in designing, developing, and maintaining large-scale distributed systems.
Preferred Qualifications 1.Familiarity with Kubernetes architecture and extensive experience in cloud-native system development; 2.Experience with at least one mainstream machine learning framework (e.g., TensorFlow, PyTorch, MXNet); 3.Familiarity with Django, Flask, or related technologies, with backend development experience; 4.Experience in one or more of the following areas: AI Infrastructure, HW/SW Co-Design, High-Performance Computing, ML Hardware Architecture (GPU, accelerators, networking), Machine Learning Frameworks, ML for Systems, Distributed Storage.
