MLB 失效分析工程師/課級主管

產品線: Server產品 

1. development CPLD for server application 

2. Server CPLD validation and report output 

3. Support EE debug

Cable 設計、系統階設計、BOM規劃、組裝規劃、Label 規劃、FRU 規劃

產品：筆電 

1. 主要負責風扇機構,流場和扇葉設計, 並需組裝Mockup樣品及測試. 

2. 需與客戶、廠商及廠內各部門人員有效率溝通, 獨當一面作業能力. 

3. 產品應用及分析能力.

伺服器產品PSU規格品選用,客製品協同開發,系統/單體驗證,除錯 

具有Server PSU 3~5年設計經驗 

熟悉AC/DC PSU架構特性 

熟悉PSU驗證操作流程 

知悉電源PSU生產流程

1.產品定義規劃 

2.線材設計 

3.產品組立SOP及零件清單BOM編寫 

4.新產品導入生產

Hardware circuit design, layout, pilot run and debug of telecomm / datacomm products. (Router / Switch / Server)

1. 協助新產品開發＆替代料導入 

2. 替代料驗証進度追蹤及記錄 

3. 零件品質管理  

4. 供應商關係管理 (供應鏈狀態/專業技術諮詢) 

5. 零件系統與流程優化與管理,包含PCN/ECN/EOL管理 

6. 2nd source零件資料庫維護 

7. 溝通與協調零件驗證事宜, 報告給 Dell CE team 

8. 2nd CM (contract Manufacturer) project (OEM)

CESBG 產品線: Server產品 

1. 伺服器BOM管理及維護 

2. 存儲器BOM管理及維護 

3. 電子元件PCN 管理作業

1. Product EMC Design and Verification test. 

2. Product EMC Issue Analysis and Debug. 

3. Product EMC World-wide Certification.

1.伺服器主板規劃與設計 

2.產品規格驗證與分析

1. 負責無線產品天線研發設計測試 

2. 新產品研發到量產的技術轉移及製程改善 

3. 新技術量測規劃(5G及毫米波量測)

協助執行 IC 設計驗證、先進封裝測試與製程數據分析。參與功率半導體研發專案的實驗採集；配合追蹤良率數據，學習車用電子零件之設計與整合規範。 

Assist in IC design verification, advanced packaging testing, and process data analysis. Participate in power semiconductor R&D data collection. Support yield tracking and learn automotive component design specs.

參與智慧醫療產品與穿戴式生理量測設備的需求分析、功能設計與產品原型測試。協助開發生醫雲端平台之應用層功能，並支援生理訊號處理演算法的初步驗證。配合推動 AI 護理協作方案於醫療場域的測試佈署，支援遠距照護平台的數據串接與日常維護作業。 

Participate in requirement analysis and prototype testing for medical wearables; Assist in developing biomedical cloud platform functions. Support AI nursing solution deployments and data maintenance.

* 負責雲端Server伺服器的相關工作 

1. DC Power測試計畫／報告撰寫與執行 

2. 替代料及PCN之評估與驗證 

3. 線路圖及Layout圖檢查 

4. 協助除錯與系統驗證 

• 計畫介紹：  

• 實習期間: 即日起-2027/6 (長期實習) 

• 職缺內容: 實習生 (大學四年級或碩士二年級在學學生) (電子相關科系佳) 

• 實習薪資: 時薪(一週3天以上)，優於基本工資 

※完整一年期滿轉正過試用期另外有奬金哦~~

Data Center Switch設計開發 

1. 資料中心交換機與DPU網路卡系統架構開發設計與硬體電子電路設計開發 

2. 產品測試驗證計畫執行與客戶認證 

3. 量產導入，處理產品良率分析與改善 

4. 跨部門團隊（PM/製造/品保）協作，確保產品品質與開發交付時程 

5. 協助客戶技術支援與問題排除 

6. 支援海外生產線(越南)相關測試設備架設與優化及自動化導入

1. BU/Vendor Engagement & PCN Management 

•Coordinate in vendor’s component samples by BU’s requested for PCN validation. 

•BU validation status and schedule follow up, needs to ensure all PCN be closed on time. 

2. Documentation & Systems Support 

•Prepare and update component qualification reports by each project developing stage, supplier HW/FW changing records, internal CE checklists. 

•Maintain FEP component database accuracy. 

•Support uploading MRR, BOM and compliance certifications (CCF/CCD) in Agile. 

3. Technical support 

•Work closely with CEs and vendors for PTRR review to ensure FEP resilience. Also participate in CE/GSM/Vendor meetings and follow up on action items to ensure FEP readiness milestones are achieved. 

•Engage with CE and vendors for Component Life-Cycle management and 2nd source analysis by different CPNs.

負責產品：車載外包廠品質管理 

1.本地供應鏈含代工廠的開發、評估和稽核（VDA 6.3 +PPAP） 

2.新增Local 物料的承認測試 

3.供應鏈/代工廠的PCN變更所需要材料Qualify和PPAP稽核 

4.協助QA/PQE 對代工廠日常和年度稽核 

5.串接海外RD和生產製造單位，處理各類物料的FA和各種應急出差

* 負責雲端Server伺服器的相關工作 

1. 協助硬體部門專案測試及報告撰寫  

2. 協助部門處理庶務事項 

• 計畫介紹：  

• 實習期間: 即日起-2027/6 (長期實習) 

• 職缺內容: 實習生 (電子相關科系佳) 

• 實習薪資: 時薪(一週3天以上)，優於基本工資 

※完整一年期滿轉正過試用期另外有奬金哦~~

＜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. 設計伺服器硬體組件（如主板、CPU、IO卡），使用CAD軟體開發電路圖。 

2. 進行硬體原型測試，執行功率和性能基準測試，優化設計。 

3. 支持製造和客服，診斷硬體故障，提供技術支援。 

4. 需要時與供應商溝通，處理設計相關的問題，確保產品品質。 

5. 替代料選用與BOM製作。