零一万物IT工程师（AI工作流方向）

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。

你将负责 1. 构建 AI-ready 内容系统 ● 设计并结构化内容知识库（如：功能知识、术语库、本地化规则等），以支持 AI 调用与生成 ● 定义内容标准、数据结构（schema）与治理框架，支撑 AI 内容的规模化输出 ● 与业务团队协作，优化内容的检索、生成与评估机制 2. 用 AI 重塑内容工作流 ● 基于 AI 重新设计端到端内容生产流程 ● 设计与优化 Prompt 策略、评估体系以及 Human-in-the-loop 流程 ● 识别并推动 AI 在效率、一致性与质量上的提升机会 3. 通过内容驱动产品与用户体验 ● 为核心产品流程提供高质量 UX 内容 ● 确保内容与买家需求、业务目标以及不同市场的文化差异相匹配 ● 与产品、设计、本地化团队紧密合作，共同打造一致的全球体验 4. 探索、验证与规模化 ● 原型化 AI 驱动的内容解决方案，探索新工具与新方式 ● 基于数据分析持续优化内容系统与输出效果 ● 参与构建面向未来的 AI 时代内容设计方法论 Key Responsibilities 1. Architecting AI-Ready Content Systems - Design and structure content knowledge bases (e.g., functional knowledge, glossaries, localization rules) to support AI retrieval and generation. - Define content standards, data schemas, and governance frameworks to enable the scalable output of AI-generated content. - Collaborate with business teams to optimize content retrieval, generation, and evaluation mechanisms. 2. Redefining Content Workflows with AI - Redesign end-to-end content production processes leveraging AI capabilities. - Design and optimize Prompt strategies, evaluation metrics, and Human-in-the-loop (HITL) workflows. - Identify and drive opportunities for AI to enhance efficiency, consistency, and quality. 3. Driving Product & UX through Content - Deliver high-quality UX content for core product touchpoints. - Ensure content aligns with buyer needs, business objectives, and cultural nuances across diverse markets. - Partner closely with Product, Design, and Localization teams to craft a cohesive global experience. 3. Exploration, Validation & Scaling - Prototype AI-driven content solutions, exploring new tools and methodologies. - Continuously optimize content systems and output performance based on data analytics. - Contribute to the development of future-proof content design methodologies for the AI era.

职位描述 1. 负责企业内部 AI 提效项目的前沿部署与落地，围绕市场、销售、客服、供应链、财务等核心业务场景，推动大模型与 Agent 能力在真实业务流程中产生效率提升； 2. 深入业务一线，梳理部门工作流、信息流、数据流和协作链路，识别高频、重复、低效、规则明确或知识密集型场景，设计可落地的 AI 提效方案； 3. 基于大模型、Agent 框架、RAG 知识库、多模态能力、工作流编排等技术，设计具备商业价值、工程可行性和可规模化复制能力的解决方案； 4. 负责 AI 项目从 Demo、POC 到生产环境上线的端到端推进，包括架构设计、核心功能开发、效果评估、性能优化、成本控制和交付验收； 5. 与数据、IT、业务部门紧密协作，将模型能力转化为具体业务工具，包括但不限于智能知识库、数据分析助手、客服助手、营销内容生成、会议纪要、自动报表、流程审批辅助、供应链异常分析等； 6. 持续沉淀内部高价值 AI 应用场景、通用组件、行业 Know-how、Agent 工作流和最佳实践，推动企业级 AI 能力平台化、组件化、规模化复用； 7. 关注 AI 工具在企业内部落地过程中的权限、安全、数据治理、流程适配和用户体验问题，推动 AI 应用真正融入员工日常工作流。

• Be a subject matter expert on databases, particularly on Relational Databases, able to discuss with customer on database modelling, migration, performance testing and day to day operations • Have wide ranging experience with open source and commercial databases such as MySQL, PostgreSQL, Oracle & SQL Server…etc • Be familiar with major cloud service providers in using it for deployment of workloads (AWS, Alicloud, GCP, Azure…etc) • Be a technical expert on all aspects of OceanBase (compatibility assessment, deployment, administration, development, migration…etc) • Facilitate introduction, discussion and demonstration of OceanBase’s technology, vision and value proposition either via individual or group sessions with customers • Engage and discover prospects’ pain points, business/technical challenges and identify how OceanBase can add value • Run Proof of Concept projects with customers to validate OceanBase’s capabilities Support post sale OceanBase implementation with OceanBase delivery team • Maintain deep understanding of competitive as well as complementary technologies and how to position OceanBase DB in relation to them • Provide guidance on how to resolve customer-specific technical challenges • Collaborate with Product Management, Engineering, and Marketing to continuously improve OceanBase product and position in the market