Working at Freudenberg: We will wow your world! Responsibilities: Assist in the development of machine learning and deep learning applications. Collect, clean, and preprocess structured and unstructured data; build high-quality datasets and perform data annotation. Apply
Job Details: Job Description: The Role and Impact As a Cloud Application Development Engineer, you will play a vital role in designing, developing, and deploying cutting-edge cloud-based solutions that enhance the user experience and streamline processes.
Role Description We are building the next generation of AI-native engineering talent engineers who use AI as a core part of how they work, not as an add-on. As an AI Engineer (Software), you will design,
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 Software Engineering General Summary: The candidate will work with a fast-paced automotive systems team working on Snapdragon SoCs for leading car OEMs for digital cockpit and ADAS/AV solutions.
Every CPU, GPU, and Tegra SoC NVIDIA has shipped in the past four years passed through our toolchain on its way to production. Over 200 product SKUs were optimized during the Blackwell generation alone! Now were
We are now looking for a Deep Learning Software QA Engineer Intern! The position is in NVIDIA Deep Learning Software Quality Assurance team that defines, develops and performs tests to validate robustness and measure the performance
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。 这个岗位适合对