Software Engineer_Diagnostics AI (Kaohsiung/Taipei)

1. 測試軟體與網頁平台開發 

2. 伺服器/存儲器測試及功能開發 

3. 軟體部屬測試環境管理規劃

1. 熟悉並了解 NVIDIA DGX/HGX/MGX/IGX 等系統架構。 

2. 熟悉並了解 NVIDIA GPU/CPU/DPU/Networking 等相關元件組成。 

3. IT infrastructure setup 

4. 叢集架構建置與效能分析。 

5. 叢集佈線環境設計。

1. Design, deploy and maintain large scale AI/HPC cluster virtualization environment (Slurm, Kubernetes, Kuberay, Ray, Helm, HTCondor, LSF, DLRover, LamaFacotry, etc) 

2. Orchestra and optimize large scale virtualization enivronment for HA and high performance 

3. Security assessment and implementation on the large scale cluster platform 

4. Scaling and automation system development

1.帶領團隊依專案時程進行軟體分析, 設計及撰寫程式 

2.指導開發人員日常技術工作 

3.設計及管理Web端及後台系統開發流程, 及尋找解決方案並提出具體策略與執行方式 

4.管理整個軟體開發專案生命週期和軟體開發品質管理 

5.依據公司策略及發展，推行各項專案及軟體開發 

6.跨部門溝通協調

Job Description: 

We are seeking a skilled and motivated AI/HPC cluster engineer to design, develop, and maintain a virtualized HPC environment and then deploy to the lab environment and the factory. This role is crucial for enabling our team to develop and test AI/HPC tools in a virtualized environment first, and after verification the complete process, to deploy it on the physical infrastructure.  

[Key Responsibilities] 

Virtual Environment: 

- Build and manage a virtual AI/HPC cluster for development and testing. 

- Configure and optimize networking between virtual nodes to emulate real-world HPC environments. 

- Collaborate with teams to ensure the virtual cluster meets testing and performance requirements. 

- Troubleshoot and resolve networking and system-level issues within the virtual environment. 

- Document system configurations, workflows, and best practices for maintaining the virtual cluster. 

Physical Envrionment: 

- realize and impelemnt the validation test cases into the cluster DUTs.

1. Profiling 

2. Performance analysis & optimization (mainly focus on CPU/GPU/Memory) 

3. Network performance analysis & optimization

Job Summary:  

• The engineers should have comprehensive knowledge of AI Large language models, such as GPT-4, and their underlying mechanisms, including an understanding of natural language processing, machine learning, and neural networks. 

Key Responsibilities: 

• Construct, train, and fine-tune large language models to meet specific organizational requirements and goals. Implement strategies for AIGC to create high-quality content. 

• Integrate AI technologies into existing products and services, ensuring they align with business objectives and enhance user experience. 

• Work collaboratively with cross-functional teams and provide technical guidance to non-technical members. Stay updated with AI advancements and educate team members on new developments. 

• Continuously monitor the performance of AI models, making adjustments and improvements based on data analysis and business needs. 

• Maintain effective communication with other teams to ensure collaborative project success. 

• Engage in ongoing learning and research to identify and implement new and appropriate Large Language Model technologies to enhance overall efficiency.

1. 研究雲儲存元件和新功能、新版本開發演進，及系統優化，進行功能導入設計 

2. 雲儲存應用服務整合設計與開發 

3. 協助存儲平台問題釐清解決與設計優化 

4. 開發自動化佈署套件 

5. 協助客戶資料中心運維技術支援

＊對於開發Java 網站有開發經驗 

＊了解Alfresco 架構與相似經驗開發。  

＊進行開發前後功能之相容測試與除錯修改、規格技術文件撰寫。  

＊協助研發網站新技術與新工具。  

＊提升自我網站前後端技術，行動應用及網路發展趨勢

＊了解虛擬化VDI 應用 

＊Restful API呼叫、設計以及程式撰寫。  

＊進行開發前後功能之相容測試與除錯修改、規格技術文件撰寫。  

＊協助研發網站新技術與新工具。  

＊提升自我網站前後端技術，行動應用及網路發展趨勢

• Major tasks focusing on data center devices, including AI accelerators, servers, storage, Ethernet switches, and power shelf. 

• Development of hardware diagnostics software 

• Development of automated testing processes for products 

• Integration or development of software for product stress testing 

• Support for manufacturing test development during new product introduction

Job Summary: 

The Machine Learning engineer will be responsible for computer vision related projects, particularly in advanced image/video processing analyses and utilized for improving product quality, production yield, and factory effectiveness, along with other R&D activities such as algorithm designs, prototype developments and experimental designs.  

Key Responsibilities: 

• Design, implement, and refine advanced Machine Learning/Deep Learning models to support product quality improvement, increasing yield and factory effectiveness. 

• Cultivate strong relationships with production line engineers, IT, and other key stakeholders to ensure alignment of modeling initiatives with company objectives and to identify new hypotheses for model improvements. 

• Scale out modeling capacity by driving infrastructure improvements such as automation of data preparations, model training, implementation, and optimization. 

• Apply deep learning and computer vision techniques, such as image classification, object detection, and support integration of models into tools for use by product engineers and business analysts. 

• Engage with customers to develop and customized data analytics solutions to address special needs. 

• Address ad hoc queries from management and present actionable recommendations in a clear, concise, and convincing manner. 

• Communicate the application and benefits of using various modeling techniques to improve decision-making to customers and stakeholders. 

• Collaborate effectively with team members, whether leading projects or supporting initiatives led by others.  

• Research and evaluate new data technology and state-of-art algorithms, and keep up to date with market trends and competitive insights.

* 開發 OpenStack 和 CEPH 的自動化佈署。 

* 開發自動化維運工具。 

* 協助客戶進行平台規劃和佈署。

團隊組成 (Team): 

前身是提供企業內部資料分析顧問服務以及打造客製化資料系統服務團隊之資料工程部門，擁有多年經驗。目前積極規畫與開發自有的資料處理平台，支撐高自由度、高效能、可靠並可擴展之資料處理/分析流程，解決實務上欲打造一套資料系統的難題。 

工作內容 (Responsibilities): 

1. Software Engineering: 

• Produce specifications and determine operational feasibility 

• Write well-designed and testable code for building an efficient and stable system 

• Troubleshoot, debug, maintain, and upgrade existing systems 

2. Data Platform Development & Data Engineering: 

• Module development and integration for building a modern data platform 

• Efficient data flow/pipeline module design and Implementation 

• Work closely with data science team to integrate ML/statistical models and analytical workflow

1. 硬體診斷軟體開發及現有模組維護 

2. 硬體壓力測試軟體開發及維護 

3. 依產品功能建構測試計畫並導入工廠

Job Summary/Role: 

The Big data developer team works closely with the manufacturing functions in all relative data fields, such as, 1) connecting and automating the data pipeline from MFG file servers, API, RDB, etc., 2) building structurally data warehouse and data market for flexible application or dashboard, and 3) also handling data cleansing and data governance. In order to accelerate the development of application/analysis, Big data developer will be the individual contributor with strong experience in the technologies of Big Data, Data warehouse, SQL and NoSQL etc. Also Big data developer will also work closely with data analysts/data scientists to solve the real MFG cases. 

Responsibility: 

• Collaborate with users, data analysts, data scientists to develop flowcharts, layouts and documents to identify solutions. 

• Architect on real MFG needs and translate requirements into technical needs. 

• Identify and understand the data sources that are relevant for analysis, and implement efficient data engineering functions (including data ingestion, cleaning, ETL, integration, etc.) 

• Consolidate the requirements and using data engineering skills to design a robust Data Mart. 

• Participate in design, architecture review and deployment of Big data and data warehouse solutions. 

• Design for automation and monitoring mechanism in a continuous integration environment. 

• Bring new data technology with POC to solve existing data challenges. 

• With high performance and comprehensive SQL to process data in relational databases (or Map/Reduce style querying for NoSQL databases). 

• Work closely with user/IT/data science team to design and develop scalable data pipeline or end-to-end analytical workflow. 

• Provide direction, training, and guidance to less experienced team members.

Job Summary 

此職位為偏軟體與資料工程導向的工程師角色，主要負責將實際使用者 (User) 需求轉化為可落地的資料流程、後端服務與視覺化應用。 

工作重心聚焦於資料處理、系統整合、效能與穩定性，並透過與使用單位的密切溝通，持續優化系統與報表，確保資料能被正確且有效地應用於實際業務場景。 

Key Responsibilities 

- 與內部使用者（如工廠、營運或相關單位）溝通需求，釐清實際使用情境，並將需求轉化為具體的系統設計、資料流程或報表規格 

- 使用 SQL、Python 進行資料處理、清洗與轉換，設計與維護穩定的資料管線（ETL / Pipeline） 

- 開發與維護後端服務或 API，支援資料存取、系統整合與應用功能 

- 規劃並開發 BI / Dashboard 視覺化報表（如 Tableau），確保資料正確性、可讀性與實際可用性 

- 與其他工程師、IT 或跨部門團隊合作，進行系統串接、資料整合與流程優化 

- 針對資料量成長或使用情境變化，進行效能調整、架構優化與問題排查 

- 持續關注資料品質與系統穩定性，主動發現並改善影響使用者體驗的問題 

※ 本職位重視將資料與系統「做穩、做對、做給人用」，而非以模型或演算法為主要解決手段。