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.
SummaryOur System Hardware Engineering Team builds and validates next-generation hardware systems. We are looking for an intern passionate about AI and software engineering to help us design and implement internal AI-powered tools that accelerate hardware design, testing, and
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
At AIA we’ve started an exciting movement to create a healthier, more sustainable future for everyone. As pioneering innovators for over 100 years, we’re now transforming our organisation to be faster, simpler and more connected. Because
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
具身Agent算法工程师-通用业务部 上海 校招 正式 技术 - 算法类 职位 ID：A68464 职位描述 设计分层/端到端 Agent 架构：负责高层任务理解（Task Planning）→ 中层行为规划（Skill/Sub-goal Decomposition）→ 底层动作执行（VLA/RL/WBC）的全栈算法设计。研发基于大模型的具身决策引擎：利用 LLM/VLM 进行任务拆解、常识推理、异常恢复（Failure Recovery）与重规划（Re-planning），实现 SayCan、RT-H、VoxPoser、CoPa 等路线的工程化落地。构建多模态感知-记忆-行动闭环：融合视觉、语言、触觉、本体感知与历史交互信息，支持长程任务中的上下文保持与状态追踪。设计机器人记忆架构：包括短期工作记忆（Working Memory，支持当前任务上下文）、长期情景记忆（Episodic Memory，存储历史交互经验）与语义记忆（Semantic Memory，存储物体属性、环境常识）。实现基于记忆的 RAG/在线学习：支持机器人在执行中动态检索历史经验、调用外部知识库，并通过真实交互数据实现策略的持续微调（Continual Learning）与自我改进（Self-Improvement）。 职位要求 计算机科学、人工智能、机器人学、认知科学等相关专业，硕士及以上学历。Agent 与 LLM：深入理解 LLM Agent 架构（ReAct、Reflexion、LATS、AutoGPT 等），具备 Prompt Engineering、Function Calling、Tool
SummaryAI & Data Platforms (AiDP) is IS&Ts engine for AI-powered innovation. The team brings together data, application development, and machine learning — including generative AI — along with data services and customer success functions, to help
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.
1. Enhance R&D efficiency through AI technology application and implementation 2. AI Application Development and Project Delivery Lead AI project requirements analysis, technical solution design, and product delivery Complete fine-tuning, alignment, and inference optimization for general
Job Description The AI Full-Stack Engineer will work closely with Product, Forward Deployed Engineering (FDE), and business teams to rapidly deliver production-ready AI capabilities and MVP tools for real-world business scenarios. Key responsibilities include: - Design, develop,
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 exposure to advanced
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。