SummaryAt Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. We believe that the diversity of our people and their ideas encourages the innovation that runs through everything we
SummaryImagine what you could do here. At Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Bring passion and dedication to your job and theres no telling what you
Company Description About Grab and Our Workplace Grab is Southeast Asias leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, weve got your back with everything.
SummaryThe Answers & Knowledge & Information team is creating groundbreaking technology for artificial intelligence, machine learning, and natural language processing! The features we create are redefining how hundreds of millions of people use their computers and
Company: Qualcomm China Job Area:Engineering Group, Engineering Group Systems Engineering General Summary: Job Overview Sensors‑based technology enables a wide range of applications, including navigation, gaming, advanced user interfaces, multimedia, and AR/VR experiences. This role offers hands‑on
SummaryAt Apple, we believe our products begin with our people. By hiring a diverse team we drive creative thought. By giving that team everything they need we drive innovation. By hiring incredible engineers we drive precision. And
我们正在寻找一位 全栈工程师，参与核心 AI Agent 产品从架构设计、能力建设到生产落地的完整过程。你将参与 Web 产品、API 服务、Agent 编排系统、后台任务系统、数据层和部署流程等多个环节，和团队一起构建面向真实生产环境的 AI Agent 产品。 你将深度参与 AI Agent 系统的工程化建设，包括 Agent Loop、工具调用、任务编排、多步骤执行、上下文管理、模型路由、异步任务处理、结果持久化、错误恢复、日志追踪与系统可观测性等关键模块。这个岗位不是简单调用大模型 API，而是需要将 LLM 能力、业务流程、前后端系统和用户体验结合起来，构建可稳定运行、可持续扩展的生产级 Agent 产品。 在这个角色中，你需要理解 Agent 如何接收用户目标、拆解任务、选择工具、执行动作、处理中间状态、根据反馈继续推理，并最终产出可靠结果。你也需要关注 Agent 执行过程中的实际工程问题，例如任务超时、工具失败、重试策略、状态一致性、成本控制、并发执行、队列调度、数据追踪以及生成结果的可复现性。 你会和产品、设计、前端、后端和基础设施团队紧密协作，把 AI 能力落到真实的产品体验中。我们希望你既能理解前端产品形态和用户交互，也能深入后端服务、数据模型、任务队列和 Agent 编排系统，帮助团队搭建长期可维护、可扩展、可观测的 AI Agent 工程体系。