AMDAI Inference Engineer

Join a dynamic team of designers, content strategists, and user researchers working on Alibaba’s global B2B e-commerce platform, Alibaba.com. We collaborate closely with product and business teams to create a top-tier online marketplace for businesses in regions like North America, Europe, and Southeast Asia. Alibaba.com is the original business of Alibaba Group, launched in 1999, and we’re focused on continuously improving it to better serve our global customers.  As a Senior UX Researcher, you will be responsible for creating, driving, and implementing research plans to discover actionable insights for creating the user experience for web-based applications and mobile devices. You will deliver the right user insights at the right time, helping project team to create digital products that are good for users and good for business. You will use your deep understanding of the methods and best practices of user research to experiment with mixed methods research to generate unique user insights and drive product innovation. You will mentor junior team mates to deliver high-quality outputs while working effectively on managing stakeholders, overlapping projects, and competing priorities. Responsibilities: • Drive ideas to improve products and services through research-driven insights and recommendations. • Independently design and execute research studies with effective research methods (e.g., desk research, interviews, focus groups, usability testing, surveys, competitive analysis, etc.), representative participant profiles, and efficient recruiting strategies. • Proactively review and perform data analysis to extract insights and actionable recommendations from qualitative or quantitative data, and collaborate with teams to translate insights into product and business impact. • Present your work at research critique sessions to collect constructive feedback from the team and leadership. • Collaborate with designers, engineers, and product managers throughout the research, design, and product development processes. • Organize post-mortem reviews of your projects with collaborating teams, reflect on outcomes, and proactively suggest improvements. • Through knowledge sharing, leading by example, and constant learning, contribute to the team’s professional growth and development. • Constantly learn and implement AI in the workflow to enhance research efficiency, and proactively identify and suggest new tools for the team. • Effectively identify and implement new ways to conduct research, grow the team’s influence, and create impact.

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。

1. Strategic Account Management & Growth Cultivate and expand relationships within assigned key accounts, using existing contacts to identify upsell and cross-sell opportunities. Develop account plans that align with clients’ business goals and SenseTime's vision. 2. Presales Leadership & Solution Design Architect and execute presales strategies that support SenseTime’s sales targets and strategic direction Drive discovery workshops, perform technical/business need assessment, and shape custom AI solutions. Deliver compelling demos, proof‑of‑concepts, pilots, and presentations to enterprise clients. 3. Proposal & Response Development Author persuasive proposal materials, decks, and technical documentation tailored to client requirements. Lead RFP/RFI submissions and collaborate with sales to ensure messaging matches client value drivers . 4. Cross-Functional Collaboration Work closely with sales, product, delivery, and marketing teams to ensure seamless alignment between customer needs, proposed solutions, and execution plans. Act as SenseTime’s subject-matter expert at forums, internal events, and external client engagements. 5. Market Intelligence & Positioning Monitor trends in AI, computer vision, and industry-specific domains. Provide insights into competitive landscapes and pricing positioning

· Lead the end-to-end development and deployment of AI-powered after-sales agent solutions for vehicle series L & i, ensuring coverage of after-sales scenarios across all user vehicles. · Identify, evaluate, and conduct on-site technical due diligence for AI Agent vendors in Silicon Valley, negotiate and finalize technology procurement contracts aligned with development requirements. · Oversee external vendors to ensure timely delivery of core Agent functionalities (e.g., multimodal interaction, self-evolving inference engines) · Drive North America-localized data training pipelines to enable cross-scenario migration capabilities for adaptive decision-making. · Attend key industry summits to identify emerging industrial AI technologies, document technical innovations, and deliver monthly cutting-edge technology briefings.