軟體研發工程師 (土城)

1. 使用者介面開發： 負責開發高品質、高效能、響應式的醫療系統前端應用程式與操作介面。 

2. 組件化架構實踐： 設計、開發並維護可複用、可擴展的標準化前端組件庫。 

3. API 數據串接： 與後端團隊協作，串接 RESTful API 或 GraphQL 接口，實現前後端數據交互。 

4. 應用程式狀態管理： 負責複雜前端應用的狀態管理機制設計與實現（如 Redux, Zustand）。 

5. 前端效能優化： 分析並優化應用的載入、渲染與運行效能，提升使用者體驗。 

6. 確保程式碼品質： 撰寫單元測試、組件測試與端到端測試，並參與程式碼審查（Code Review）。 

7. 前端工程化建設： 維護專案的建構工具鏈（Webpack/Vite）與 CI/CD 流程。 

8. 瀏覽器兼容性與調適： 確保應用在主流瀏覽器與不同設備上的兼容性與一致性。 

9. 跨團隊協作： 與 UI/UX 設計師、產品經理、後端工程師緊密合作，將產品需求高效落地。

1. 負責雲端平台的架構設計與開發實現，支撐端雲一體化整合方案的技術落地，保障雲端系統與終端設備的高效協同及穩定運行。 

2. 參與慢病管理平台、醫院HIS系統數據通道等基礎架構的雲端技術搭建，設計並實現FHIR協議數據互通的雲端解決方案，確保全場景數據流暢傳輸與交互。 

3. 基於客戶需求，進行雲端系統的定制化開發與優化，參與解決方案及系統設計說明書中雲端部分的編寫，明確雲端應用與設備端產品的整合邏輯。 

4. 與FAE、業務團隊協作，理解客戶在端雲項目各階段的雲端需求及問題，參與解決方案的討論與制定，跟進雲端功能的開發與交付進度。 

5. 制定雲端平台的交付規格，明確技術標準與FHIR&DICOM協議接口規範，確保雲端系統交付符合客戶預期，同時配合項目團隊完成雲端部署的技術支持。 

6. 編寫雲端開發相關的技術文件，包括微服務架構設計、接口說明、開發指南及TFDA相關資安文件，參與客戶的雲端解決方案簡報，根據反饋調整雲端技術方案。

＜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

電動車類/車用電子/儲能 

1.熟悉BMS/BMU軟體架構 

2.負責各種上位機架設以及調適 

3.負責產品法規和廠規測試,驗證以及除錯  

4.協助處理客戶端問題及現場技術支援

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

設計與實作智慧城市平台所需的後端API服務，確保資料安全、效能與穩定性。 

1. 熟悉RESTful API設計與OpenAPI規範 

2. 熟悉Node.js/Go/Python等語言 

3. 熟悉容器化技術與Kubernetes 

4. 熟悉CI/CD與API安全機制

開發AI模組以支援智慧城市應用場景（如客服、預測、流程優化），並進行模型部署與測試。 

1. 熟悉Python與深度學習框架（如PyTorch、TensorFlow） 

2. 具備ML/DL模型部署經驗 

3. 熟悉MLOps與雲端運算平台 

4. 有實務專案經驗尤佳

1.Server性能及健康監控程式開發(Prometheus, Grafana..) 

2.Server測試自動化程式開發(Redfish, IPMI, SNMP, BMC...) 

3.Server OS 鏡像自動化開發, 訂製及部署

•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

1. uFEI BIOS porting 

2. BIOS debug

CESBG-產品線Server 

1. 伺服器BIOS軟體開發及維護 

2. 客戶規格審閱 

3. 專案問題分析及解決 

1.BIOS development engineering feasility estimation  

2.BIOS product specification definition  

3.BIOS engineering specification creation  

4.BIOS development  

5.BIOS debug and mantainance

1. Responsible for Windows driver management for assigned projects  

2. Windows driver issue analysis 

3. Cross-team and company collaboration

1、具有Web程式開發經驗。 

2、會使用Javascript、CSS、HTML5 

3、有相關前端framework經驗 ex：vue.js、flutter 

4、熟悉RWD、排版、網頁架構