AI Server-EDA /演算法應用開發工程師(土城)_E事業群

1.解析伺服器硬體架構與線路設計，掌握系統運作原理。 

2.研究並實作 BMC 與各式硬體介面的通訊協定。 

3.在 Linux 環境中進行開發與除錯，熟悉 Git、伺服器建置及開發工具操作。 

4.使用 OpenBMC 平台進行功能開發、系統整合與驗證，持續優化系統穩定性與可維護性。 

5.與跨部門團隊合作，協調軟硬體整合進度，並協助工廠進行問題分析與除錯，確保產品品質與量產穩定性。

1. Embedded Linux development on ARM/MIPS SOCs.  

2. Linux technology and GNU tool chains. 

3. Experience of developing device drivers or kernel modules.  

4. Cloud Server, Storage Server, Data Center Server & Micro Server firmware development.

嵌入式系統軟韌體開發/產測程式開發BMC 韌體開發

1. Lead the lead of firmware team for server project.  

2. Study technical issue for project‘s needs. 

3. Review & sign off requirement specification. 

4. Review engineer design. 

5. Review function/interface specification. 

6. Review bug fixing. 

7. Help the design and review the common definitions for debugging and profiling.

您將共同進行以下工作項目:  

1. DPU 韌體開發  

2. NVMe over RDMA系統分析與設計  

3. 分散式網路儲存系統效能優化  

4.開源軟體開發與測試  

5. Redfish API開發維護

主要職責： 

開發各種新世代寬頻通訊設備之軟體/韌體。 

1. 資工/電子/電機/通訊系所專業尤佳，熟C/C++、Java、Linux、Embedded System、TCP/IP等網路通訊協定 

2. 二年以上網路通訊產品軟體開發經驗尤佳

【工作職責】 

•負責 SAP ABAP 程式與 SAP Fiori 應用系統之設計、開發與維護。 

•與功能顧問及技術團隊密切合作，蒐集並分析業務需求，提供專業技術解決方案。 

•執行系統分析、程式除錯與問題排查，確保系統穩定運作並有效解決技術問題。 

•持續關注並學習 SAP ABAP / Fiori 相關之最新技術、工具與最佳實務。 

【Responsibilities】 

•Design, develop, and maintain SAP ABAP programs and SAP Fiori applications. 

•Collaborate with functional consultants and technical teams to gather business requirements and deliver effective technical solutions. 

•Perform system analysis, debugging, and troubleshooting to identify root causes and resolve technical issues. 

•Stay up to date with the latest SAP technologies, tools, and best practices related to ABAP and Fiori development.

1. Web 程式設計與開發。 

2. Web 介面設計與開發。 

3. 開發與整合Kubernetes相關服務。

1. 主機板測試程式開發(伺服器，儲存器) 

2. Linux測試平台及模組維護 

3.Windows測試平台及模組維護 

4.自動化測試軟體開發 

5.系統壓力測試軟體開發

1. 系統平台上的使用者介面視覺設計  

2. 以使用者中心設計為原則，設計直覺又優美的使用者介面  

3. 使用者介面上的共用元件視覺設計及相關使用文件產出  

4. 軟體介面上所需的圖像及動態設計  

5. 使用者介面設計的易用性評估與改善

您將共同進行以下工作項目: 

1. HPC軟硬體集群架構規劃 

2. 網路系統設定與管理 

3. HPC 叢集建置與管理自動化

* 雲平台產品的售前與售後支持 

* 提供維運團隊技術支援 

* 對用戶需求做分析及提供解決作法 

* 對團隊內部及合作單位進行教育訓練

1. 規劃伺服器產品自動化測試方案 

2. 開發伺服器產品自動化測試程式 

3. 負責導入實驗室或工廠自動化測試 

4. 開發客戶或實驗室需求的軟體工具 

5. 協助開發研發單位自動化測試需求

負責產品：網路交換機/網卡 

主要職責： 

1.架設與維護自動化測試機台，確保系統穩定與可擴充性 

2.開發與維護測試腳本（Shell Script、Python），提升測試效率 

3.在 Linux 平台上進行系統安裝、配置與效能調校 

4.整合測試工具與框架，支援持續整合 (CI/CD) 流程 

5.分析測試結果，撰寫報告並提出改善建議 

6.與研發團隊合作，設計並優化測試流程 

7.支援越南工廠測試平台建置與維運（需配合出差）

To design, develop, and integrate diagnostic software solutions for servers, storage, and AI accelerator platforms, enabling automated testing and efficient issue resolution in collaboration with hardware and firmware teams. 

· Design and implement diagnostic software for hardware platforms. 

· Develop automation tools and scripts for testing and validation. 

· Integrate diagnostics with production and lab environments. 

· Troubleshoot hardware and software issues with cross-functional teams. 

· Document diagnostic procedures, tools, and technical guides. 

· Evaluate and adopt new tools or technologies to enhance diagnostics. 

Required Skills / Tools / Experience 

· Proficiency in Shell Script, Python, C/C++ 

· Strong Linux environment usage (Ubuntu preferred) 

· Knowledge of system programming, networking, algorithms, and data structures

We are seeking a highly motivated Software Team Leader, who combines technical leadership, system-level thinking, and cross-functional collaboration with R&D, Operations, and global customers to enable efficient diagnostics, automation, and intelligent data integration. 

Responsibilities 

•Lead the software team to design, develop, and integrate diagnostic software solutions for AI server and storage platforms. 

•Build a unified Diagnostics OS that supports modular and reusable test components across multiple products and customers. 

•Drive automation, standardization, and integration with production lines. 

•Collaborate with R&D and Operations to enable efficient issue diagnosis and resolution throughout the product lifecycle. 

•Develop and maintain automation tools, test scripts, and diagnostic libraries for manufacturing. 

•Define and enforce standard interfaces and log formats for seamless integration with the operation flow. 

•Document diagnostic procedures, APIs, and best practices. 

•Evaluate and adopt new tools or technologies to enhance testing efficiency and data analytics. 

Qualifications 

• Strong communication and problem-solving skills, able to respond quickly to customer and factory issues from different time zones. 

• Solid understanding of server system, AI server architecture, and diagnostics workflows. 

• Experience leading software teams and coordinating with cross-functional teams for global customers. 

• Proficient in software development lifecycle management and quality assurance practices.

1.AI / Compute / Storage伺服器測試軟體開發及相關軟體模組維護(含 板階、系統階、櫃階 測試 

2.Compute / Storage伺服器壓力測試軟體開發及維護:主要為系統階測試 

3.依伺服器組件、功能、出貨規格 建立 測試計劃，完成測試軟體套件開發後導入工廠端交付測試執行團隊: 

依專案 需短期出差美國、墨西哥 (1個月/次，年累積三個月以下)

＜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.

開發各種新世代寬頻通訊設備VoIP, ADSL, WLAN, Cable Modem, Ethernet、Home Lans, Internet Appliances, STB, WiMax, IP Phone, Video Phone, Smart Phone ....之軟體/韌體