轉職熱搜工作

協助研發人形機器人與 AMR 的控制模組；參與嵌入式電路的設計繪製與整合測試。配合優化機器人感知系統之演算法，學習在虛擬環境中進行物流方案模擬。 Assist in humanoid and AMR control module development; Participate in embedded circuit design and testing. Support perception algorithm optimization and learn logistics path simulation in virtual environments.

1.圖文管理與審閱 ：收集工程圖面、執行標準審核及資料建置作業。 2.工程料號建立 ：建立設備零件等相關料號，確保物料屬性分類的正確。 3.請採購與驗收：進行設備維護及工程請採購作業。 4.備品物料管理 ：統計與清查備品，確保備品清單與實務數量品項正確。 5.數據統計與分析：收集年度故障時數及維護費用，並確認數據統計的正確性。 ※榮獲《HR Asia》2023年亞洲最佳企業雇主獎 ※連續六年「公司治理評鑑」前5％績優公司 ※2022 台灣永續績效獎及報告書白金獎 ※台灣50大集團：第32名（2020天下雜誌） 完善的員工制度與相關福利: ◆優渥薪資及獎金制度(含年終獎金、績效獎金及員工分紅入股計劃)　 ◆完善教育訓練制度(職前訓練/在職/內外專業技能培訓...) ◆完善考核及多元化升遷管道　 ◆現代化軟硬體辦公設備(全面自動化/網路化電腦設備)　 ◆人性化管理及工作環境(廠區備有交通車及員工餐廳等設施)　 ◆優於勞基法的休假制度　 ◆勞健保、團保(含壽險、意外傷害險、住院醫療險、癌症險等)　 ◆職工福利委員會(福委會三節禮金、員工旅遊、慶生禮金、子女教育獎助金、無息貸款、婚喪喜慶補助)

＜About the Job＞ Working on top of hardware provided by robot vendors, you will be responsible for integrating our internal AI vision and decision models into real-world robotic systems. You are the technical implementation core that connects the 〝AI Brain〝, 〝Simulation & Validation〝, and 〝Physical Hardware〝 layers. Your mission is to bridge the gap between AI models and the physical world, ensuring our robotic systems operate reliably in industrial environments. You will operate with a high degree of autonomy, collaborate as an equal partner with the AI modeling team, and own the end-to-end success of the integration layer. ＜Job Responsibilities＞ 1.AI model integration & motion control ．Receive high-level action commands from AI models and perform kinematic solving and trajectory planning. ．Implement collision detection and safety mechanisms to ensure AI-generated actions execute reliably in real industrial environments. ．Maintain spatial calibration and coordinate consistency between visual inputs and action outputs. 2. Vendor interface wrapping & control implementation ．Rapidly interpret and integrate various interfaces and SDKs provided by robot vendors. ．Encapsulate proprietary vendor APIs into standardized interfaces consumable by internal systems. ．Manage communication between edge computing devices and robot controllers, resolving stability issues encountered during real-machine testing. 3. Simulation validation & real-world deployment ．Build robot simulation environments for industrial scenarios and complete stress tests and edge-case validations before deploying to physical machines. ．Establish AI model inference pipelines on edge devices and continuously optimize end-to-end performance. 4. On-site debugging & exception handling ．Conduct real-machine testing on-site, systematically collecting failure cases and data to feed back to the AI modeling team for continuous improvement. ．Implement state machines and exception handling logic to ensure safe degradation and recovery in uncertain situations. ＜Minimum Qualifications＞： ．5+ years of experience in robotics systems, automation equipment, or hardware/software integration. ．Solid robotics middleware development skills, with hands-on experience integrating third-party SDKs and hardware interfaces. ．Proven experience in real-machine deployment or on-site testing; able to independently diagnose and resolve hardware communication issues. ．Proficient in Python and C++; able to rapidly produce production-ready code within an existing architecture. ．Solid understanding of Computer Vision and 3D spatial geometry, with practical experience in sensor calibration and coordinate transformations. ．Strong independent troubleshooting skills in Linux and Docker environments.

＜About the Job＞ We are looking for a highly motivated and skilled AI Infrastructure Engineer with strong hands-on experience in Kubernetes (K8s), particularly in supporting AI/ML workflows. In this role, you will be instrumental in designing, implementing, and maintaining robust, scalable, and high-performance Kubernetes-based infrastructure that supports the entire lifecycle of our AI applications—from data processing and model training to deployment and monitoring. You will work closely with data scientists, AI/ML engineers, and DevOps teams to ensure seamless integration of AI/ML workloads within cloud-native environments. The ideal candidate has a deep understanding of container orchestration, distributed systems, and MLOps practices, and is passionate about building efficient, reliable platforms that enable rapid AI innovation. This is a unique opportunity to work at the intersection of AI and cloud infrastructure, contributing to next-generation systems that power intelligent applications at scale. ＜Job Responsibilities＞ ．Design & Architecture: Design, build, and scale a reliable and efficient Kubernetes platform optimized for AI/ML workloads. This includes provisioning GPUs, managing resources, and ensuring optimal performance for computationally intensive tasks. ．Infrastructure Management: Manage the entire Kubernetes cluster lifecycle—from provisioning and configuration to ongoing maintenance, monitoring, and troubleshooting, ensuring high availability and scalability. ．Deployment & Automation: Develop and implement CI/CD pipelines to automate the deployment, scaling, and updating of machine learning models and AI services. Ensure seamless integration with AI tools like Kubeflow, MLflow, and Argo Workflows. ．Performance Optimization: Continuously monitor and optimize system performance, focusing on resource utilization, latency reduction, and improving the overall efficiency of AI workloads. Ensure high availability and minimal downtime for AI services. ．Collaboration & Guidance: Work closely with data scientists, ML engineers, and cross-functional teams to understand their infrastructure requirements and provide technical solutions to meet workload demands effectively. ．Security & Compliance: Implement best practices for cluster security, including network policies, access controls, and vulnerability management to safeguard sensitive data and maintain compliance. ．Cost & Resource Efficiency: Manage resources effectively to optimize cost while maintaining high-performance infrastructure for AI model training, inference, and data processing. ＜Skills & Qualifications＞ ．Kubernetes Expertise: You should have hands-on experience with Kubernetes (K8s) architecture, including deploying applications, managing resources, and troubleshooting complex cluster issues in a production environment. ．Containerization & Linux Environment: Strong knowledge of container technologies such as Docker, along with hands-on experience in Linux environments. Expertise in container orchestration and deployment practices is highly valued. ．AI Workloads: Deep understanding of GPU scheduling and performance optimization, including strategies for resource allocation, workload balancing, and maximizing throughput for AI/ML tasks. ．Automation & CI/CD: You need practical experience with building and managing CI/CD pipelines using tools like GitLab CI, Jenkins, GitHub Actions, or ArgoCD to automate deployments. ．Programming & Scripting: Proficiency in at least one scripting language (e.g., Python, Bash) is a must. ．Networking: Knowledge of container networking and service mesh technologies (e.g., Istio, Linkerd) is highly desirable and a great advantage.

1. 設備潤滑保養及定期巡檢及年度保養執行 2. SOP制定與執行管理 3. 設備管理及表單管理 4. 系統改善及方案制定／設備異常分析與改善

我們是一家專注於配電箱体及各式金屬鈑金加工製造的企業，提供高品質的各式電力箱体產品及技術服務，致力於為客戶提供專業化的設計與繪圖解決方案。 工作內容： 1. 根據顧客產品需求，進行各式電力箱体產品和零組件的設計與繪圖。 2. 使用CAD相關繪圖軟體，進行拆圖及技術圖紙的繪製、修改及輸出。 3. 審核設計圖紙，確保其符合相關工程標準和規範。 4. 分析規格，評估設計是否符合法規、技術標準與成本效益。 5. 協助產品設計問題的排查與改進，包括細節修改與優化。 6. 與客戶討論相關開發需求，提供技術建議，確保設計滿足實際應用需求。 7. 建立並維護產品技術檔案及標準，確保內部文件準確性與一致性。 8. 支援研發工程團隊進行新產品設計，改良現有設計以提升製造效率與產品性能。 9. 其他研發工作 歡迎有志於配電箱体及各式金屬鈑金工業研發設計的專業人士，加入我們的團隊。讓我們攜手推動精良技術進步，共同創造未來的價值。立即投遞履歷，我們期待您的加入！