Diagnostic軟體開發主管

Ethernet Switch Development : 

1. Linux related device drivers 

2. Linux Kernel/driver/BSP Porting and Integration 

3. BMC/IPMI related firmware

1. 根據客戶應用需求,提供雲端產品技術整合之系統架構,提供符合客戶需求之產品服務 

2. 跨部門協調研發及生產單位,提供系統整合產品服務 

3. 研究及分析雲服務市場需求並設計符合客戶需求之產品

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

參與電腦視覺與 NLP 技術研發，協助開發領域專屬模型。配合設計 AI 應用框架，支援將生成式 AI 導入實際製造場景；協助執行數據標註與推理效能評估。 

Participate in CV and NLP R&D; Assist in domain-specific model development. Support AI application frameworks and GenAI implementation in manufacturing; Assist in data labeling and inference evaluation.

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

1.參與伺服器平台軟體與韌體功能之開發與維護，包含系統設計、實作與驗證。 

2.分析伺服器硬體架構與線路設計，掌握系統運作原理與軟硬體整合方式。 

3.開發與實作各式硬體介面與通訊協定（如 I²C、SPI、UART、PCIe 等），確保系統穩定運作。 

4.於 Linux 或嵌入式環境中進行開發、除錯與效能優化，熟悉版本控制與自動化流程。 

5.撰寫並維護技術文件，確保軟韌體設計具可追蹤性與可維護性。 

6.與跨部門團隊（硬體、測試、工廠製造等）協作，推進專案開發進度與功能整合。 

7.協助工廠端進行問題分析與除錯，支援產品導入與量產穩定性改善。

(embedded Linux experience acceptable if no prior OpenBMC exposure). 

2.Implement and integrate low-level drivers (U-Boot, Device Tree, Kernel), covering I2C, SPI, PMBus, GPIO, and other interfaces. 

3.Build and maintain BMC sensor monitoring, FRU/SDR tables, fan/power control, and event log systems. 

4.Implement management protocols and firmware update mechanisms (IPMI, Redfish, PLDM, MCTP). 

5.Integrate Secure Boot / TPM / SPDM security frameworks. 

6.If experienced with NVIDIA HGX/DGX/NVSwitch projects, support GPU power/thermal control, firmware updates, and OOB management integration. 

7.Prepare design documents, test plans, mass production support materials, and debug reports.

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.

1. 與客戶溝通產品規格 

2. 分析測試單位提出的顯示器問題並且與客戶說明 

3. 產線指令撰寫 

4. 與廠商討論並解決測試單位的問題

1. 部門人力能力規劃 

2. Linux環境下相關軟體開發 

3. Server量產測試軟體開發

1. 部門人力能力規劃 

2. 整合軟韌體開發資源 

3. 客戶軟韌體開發對接窗口

1. 部門人力能力規劃 

2. 客戶軟韌體量產導入服務 

3. 量產後軟韌體第一線維護及工程對接

1.維持與客戶良好關係，協調對接客戶開發/FA需求 

2.參與部門團隊建設，管理團隊事務，進行總結沉澱，帶動團隊進步  

3.參加專案日常會議，了解專案生產狀況，需要時進行工作匯報 

4.跨部門溝通協作，推動問題解決 

5.獨立完成問題的匯總、報告和追蹤，確保問題清晰落地  

6.負責各種簡報的製作及匯報

1. 建構 x86 / ARM 平台之 Linux RTOS 執行環境 

2. 設計與整合 EtherCAT / Modbus 等通訊協定與驅動模組 

3. 串接伺服器、感測器、相機等設備通訊控制 

4. 負責控制模組與 AI 模型的整合部署與環境建置

1. 開發人形機器人之雙臂／雙足運動控制與任務決策模型 

2. 設計 PD / Impedance / Model Predictive Control 等控制器 

3. 訓練強化學習與模仿學習模型，實現決策與感知行為 

4. 使用 Isaac Sim / Gazebo 等模擬平台驗證 Sim2Real 效果 

5. 整合 AI 模型與控制模組，執行實體部署與系統測試

- 推動跨系統和模塊的集成，解決架構相關技術問題 

- 架構設計: 設計Genesis Store 架構模塊並與IT/OT組件集成 

- 與開發團隊保持密切溝通交流，設置檢查要求和節點，推動用例標準化 

- 生態合作對接: 確定IT/OT架構與生態解決方案之間的一致性和匹配性 

- 知識庫與Token*相關的系統建設: 負責相關的系統設計與建設 (如: 知識管理、Token*管理) 

*Token: 透過GenAI技術整合各種跨事業群的生產相關資料，如: 語句、圖像後的數據單位，有助在不同場景下訓練AI引擎

1.電路板開發、産品電路設計、PCB布局審核、産品測試除錯、硬體與機構成本分析 

2.負責EMC測試、修改與報告 

3.與硬體及機構工程師合作進行跨部門合作協調 

4.與客戶溝通協調，協助客戶解決EMC相關問題

•開發和維護EDA應用雲服務 

•使用Python和Git進行開發和版本管理  

•利用Python和資料結構及Algorithm進行資料處理分析  

•開發和維護Dash Web/Restful API  

•使用Yolo8和sentence-transformer進行finetune應用  

•使用OpenCV進行圖像處理應用  

•使用LLM/ VLM進行語言模型應用