高雄軟體園區-分散式系統效能分析工程師

• 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.

1.數據監控Web程式開發與設計 

2.雲端應用服務功能開發 

3.使用者介面程式開發 

4.軟體模組開發與整合

* 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。

1.監控系統程式開發與設計 

2.弱電設備安裝測試維護 

3.環控系統安裝測試維護

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

我們是一個專注在提供資料分析解決方案、顧問服務及平台開發的團隊，成員組成主要包含統計、資料探勘、機器學習、資料庫、數據分析網站平台開發、資料視覺化等背景。我們徵求有高度企圖心的軟體開發工程師來共同打造世界一流之產品，發揮資料科學的商業影響力 

Responsibilities: 

• 網頁平台介面開發 

• 網頁平台API 開發 

• 分析資料整合與串接

1. 負責系統平台之 UI/UX 設計 

2. 規劃監控平台之操作流程、資訊架構與使用者體驗 

3. 製作 Wireframe、Mockup 與 Prototype，並與工程團隊協作落地實作 

4. 理解設備資料 (如溫度、流量、告警等)，轉化為直覺化視覺呈現 

5. 協助建構機房或設備之 3D 視覺化呈現 (如空間配置、設備定位) 

6. 製作產品介紹影片、操作教學或訓練素材

1. Android 原生開發：主導 Android (Kotlin/Java) App 的開發與維護，確保產品效能與穩定性。 

2. 架構翻新與重構：主動針對 Legacy Projects 進行評估，推動架構重構與優化，將「義大利麵條程式碼」轉化為高內聚、低耦合的系統。 

3. 規格驅動開發 (SDD)：落實規格驅動開發理念，投入更多精力於軟體設計，以降低重複性的人工編碼負擔。 

4. 硬體與系統整合：串接外部硬體電路（如：人臉辨識平板、門禁感測設備），處理硬體溝通協定與底層資料傳輸。 

5. 品質保障與維運：建立自動化測試（Unit Test 等）與 CI/CD 流程，負責版本除錯與上架維運。 

6. 技術文件撰寫：詳實記錄技術決策手冊及使用者操作手冊，確保知識傳承。

我們正在招募雲平台 Site Reliability Engineer（SRE），負責私有雲平台（OpenStack + Ceph）的穩定性、可用性與可擴展性。 

此角色將深度參與雲平台的設計、維運、自動化與事件管理，透過工程方法提升系統可靠度，而非單純人工作業。 

核心職責（Responsibilities） 

1.負責 OpenStack 雲平台 的穩定性與可用性維運 與 Ceph 分散式儲存 的健康度、效能與容量管理 

2.建立與維護： 

監控（Monitoring） 

告警（Alerting） 

事件回應流程（Incident Response） 

3.進行： 

故障排查（Troubleshooting） 

根因分析（RCA） 

問題事後改善（Postmortem） 

4.透過 Shell / Python 進行自動化，降低人工操作風險 

5.與平台開發、網路、硬體團隊協作，改善整體系統可靠度

我們正在尋找具備雲平台實務經驗的 FAE 工程師，主要負責 OpenStack + Ceph 私有雲平台的客戶技術支援、問題排查與架構說明。 

此職位需能在客戶現場或遠端協助平台部署、維運與障礙處理，並能透過腳本與簡易開發提升支援效率與平台穩定性。 

1. 雲平台 / 儲存基礎 

a. 具備 OpenStack 實務經驗（至少操作或維運過） 

i. 熟悉常見元件：Nova、Neutron、Cinder、Keystone（不要求全精通） 

ii. 了解基本架構與節點角色（Controller / Compute / Storage） 

b. 具備 Ceph 使用或運維經驗 

i. 熟悉 RBD，了解 OSD / MON 基本概念 

ii. 能協助判斷常見容量、效能或異常狀況 

2. 系統與支援能力 

a. 熟悉 Linux 系統操作 

i. 日誌查看、服務管理、效能觀察、問題排查 

b. 具備基本網路概念 

i. TCP/IP、VLAN、Bonding（Overlay Network 有概念即可） 

c. 具備客戶支援或跨部門協作經驗 

i. 能清楚說明問題、同步狀態、協助客戶釐清需求 

3. 腳本與自動化 

a. 能撰寫 Shell Script 

i. 用於部署、巡檢、日常維運或問題快速排查 

b. 具備 Python 基礎能力 

i. 能修改或撰寫簡單工具（如 API 呼叫、資料整理） 

c. 熟悉 Git 基本使用

We are seeking a highly motivated Software Engineer, who support the development, integration, and maintenance of the AI-driven software platform used in GPU server manufacturing and diagnostics.  

This role focuses on log processing, service development, model implementation, data analysis, and integration testing.  

The engineer will work closely with team members to implement features that improve failure attribution, test optimization, and automated diagnostics for AI server platforms.  

**Responsibilities  

1. Software System Development Implement backend service modules for the system. Develop log parsing tools, data transformers, LLM, and rule-based diagnostic logic. Write clean, maintainable code (primarily Python) and participate in code reviews.  

2. Log & Data Handling Build and maintain scripts for collecting, cleaning, and indexing diagnostic logs. Manage log storage and retrieving data for model training.  

3. System Integration & Testing Support integration between the platform modules and Diagnostics software, automation frameworks, and factory systems. Help deploy updated components to test environments or production lines.  

4. Issue Investigation Work with senior engineers to analyze failure logs, detect patterns, and refine rule-based logic. Document root causes and support continuous improvement of diagnostics accuracy.  

5. Documentation & Team Collaboration.