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  • 【無人機 Drone】品質工程師_02

    面議(經常性薪資達4萬元或以上) 40000元 桃園市蘆竹區 3~4年工作經驗
    1. 確保品質管控機制與試產穩定進行與追蹤。 2. 參與專案會議,與客戶溝通並完成品質相關需求。
  • 【PI-DQE】可靠性測試工程師

    年薪 1000000元 新北市土城區 工作經歷不拘
    1.可靠度測試執行與實驗室日常管理 2.測試治具設備開發及維護管理 3.產品可靠度測試評估與改善
  • 【AI Server / Rack】IQC 工程師_台灣NPI中心

    面議(經常性薪資達4萬元或以上) 40000元 桃園市平鎮區 3~4年工作經驗
    1. 進料品質體系管控:執行並維護 IQC 檢驗標準與作業程序,確保 AI 伺服器關鍵零組件(涵蓋 PCBA、金屬沖壓機構件、電源與散熱模組)入料符合設計與功能規格。 2. 進料檢驗執行與防堵:落實日常進料檢驗作業與抽樣計畫,確保檢驗數據的正確性與 MES 系統記錄,防堵不良物料流入 L10 系統組裝與 L11 整機櫃產線。 3. 異常初步偵測與回報:處理進料品質異常,開立異常單(CAR/MRB)並協助收集不良品的物理或電性測試數據,及時通報 SQE 與內部相關部門。 4. 品質流程優化:協助評估進料檢驗之有效性,優化日常檢驗作業流程,提升檢驗效率與防呆準確度。
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  • 光通訊光學工程師/主管(越南)

    面議(經常性薪資達4萬元或以上) 40000元 東南亞越南 4~5年工作經驗
    1、負責光纖光學零組件、光模組清潔檢測、光學組裝測試與驗證、數據分析等。 2、協助量產導入,處理光學問題分析與改善。 3、與跨部門團隊(PM/製造/品保)協作,確保產品品質與時程。 4、支援越南生產線相關光學測試設備搭建與優化,自動化導入。 5、協助客戶技術支援與問題排除。
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  • 台灣電池中心-生產品質工程師(高雄) 2026Q002

    面議(經常性薪資達4萬元或以上) 40000元 高雄市大寮區 3~4年工作經驗
    ★工作地安排:工作地於高雄大寮,視專案及產品會有國內外出差需求★ 歡迎有志者一同加入! 1.管理生產品質檢驗 2.負責現場的4M變更管理 3.不合格品管理 4.統計製程管制 5.糾正與預防措施管理 6.主管交辦事項
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  • 【QA】iPEBG 品質管理主管

    面議(經常性薪資達4萬元或以上) 40000元 新北市土城區 5~6年工作經驗
    1.目標管理:品質KPI目標達成與管理、品質成本管理; 2.品質管理:依據SIP/IS文件參與NPI/MP首檢/巡檢/終檢/出貨檢驗工作,落實紅線參數及紅線底落; 3.異常追蹤:執行品質異常處理方案,落實品質管制要求決議; 4.制程巡檢:按照檔執行並定期確認並針對對異常結果進行糾正處置; 5.數據解析:結合現場工程改善及良率提升的實際需求,統計分析為工程決策及現場改善提供數據支撐; *須配合出差
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  • 台灣電池中心-(夜班)品檢組長(高雄) 2026Q001

    面議(經常性薪資達4萬元或以上) 40000元 高雄市大寮區 3~4年工作經驗
    1.負責儀器操作及檢驗工作 2.產線異常反饋及異常單填寫 3.檢驗日報表整理 ★工作地於高雄大寮 ★此職務為班別為初期三班制,依照產線規劃會調整為四班二輪(做二休二)班別,可配合常夜優佳 三班制(早):08:00~16:30 三班制(中):16:00~00:30 三班制(夜):24:00~08:30 四班二輪(日):07:30~19:30 四班二輪(夜):19:30~07:30 津貼如下: 績效獎金:0~2000元/月 全勤獎金:0~1500元/月 輪班津貼: 中班:200元/天 晚班:450元/天
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  • 品質暨系統稽核管理(資深)專員/主管

    面議(經常性薪資達4萬元或以上) 40000元 新竹市 3~4年工作經驗
    負責CNSBG 品質暨環境管理系統 1.各體系品質暨環境管理系統的規劃與維護(TL9000/ISO9001/ISO14001) 2.ISO 文件審核、定期執行 ISO 內部稽核及主導外部稽核 3.執行港、澳、台關鍵供應商RBA稽核管理 4.車載供應鏈的品質管理系統評鑑與稽核 5.各廠區Six Sigma 綠帶培訓與專案執行規劃 6.儀校設備的管理與改善 7.跨部門溝通與協調能力 8.執行主管交辦的任務
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  • 【AI Server / Rack】客戶品質主管_台灣NPI中心

    面議(經常性薪資達4萬元或以上) 40000元 桃園市平鎮區 5~6年工作經驗
    1. 客訴與異常追蹤:管理客戶抱怨案件流程,確保品質異常分析報告能有效落實並回饋至生產端,推動持續改善。 2. 跨部門根因分析:主導品質異常之解析,召集跨部門(RD/ME/PE)釐清 L10 系統級或 L11 機櫃級之失效真因,產出具公信力之 8D 或 5C 分析報告。 3. 矯正措施落地與回饋:確保品質異常分析報告之防堵與改善對策能有效落實,並橫向展開回饋至前端生產端與供應鏈,推動製程防呆與持續改善。 4. 客戶滿意度追蹤:彙整客訴數據與品質指標(KPI),定期參與客戶之品質績效審查會議(QBR),提升客戶對廠區品質之信賴度。
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  • AI Project Manager/System Analyst (Data Science & AI Team)

    面議(經常性薪資達4萬元或以上) 40000元 新北市土城區 5~6年工作經驗
    <About the job> As the AI Project Manager and System Analyst, you‘ll join the various advanced Data Science & AI Projects in the corporate headquarters. In addition to developing intelligent applications via related AI and Advanced Data Analytics Technology for digital transformation, you will have plenty of opportunities to develop emerging AI applications based on different use cases and expand your tech skillset in this world-class company. <Job Description> *Type1:AI Project Manager Manages AI-focused projects from initial planning to delivery, ensuring alignment with business goals and promoting innovation. Responsibilities include coordinating cross-functional teams, allocating and optimizing resources, and maintaining project schedules and budgets. The role requires proactive problem-solving to address project challenges and mitigate risks while driving effective communication across departments. <Qualifications> 1. Over 5 years of experience in software project management, with a strong background in Agile methodologies. 2. Highly proactive, self-motivated, and accountable. 3. Willing and able to travel internationally for approximately 1-2 months per year. <Required Skills> 1. Strong understanding of the project lifecycle, including planning, execution, monitoring, and closure. 2. Proven ability to manage project timelines, budgets, and resources effectively, ensuring projects are completed on schedule and within budget.t. 3. Ability to communicate effectively with individuals from different departments and roles and coordinate resources. 4. Strong analytical and problem-solving skills, with the ability to swiftly assess complex issues and implement solutions. 5. Mastery of Microsoft Office programs (Word, Excel, PowerPoint) (Nice to have, but not necessary) 1. Familiarity with manufacturing processes 2. Possession of project management certifications such as PMP or Scrum. 3. Intermediate or above English proficiency in listening, speaking, reading, and writing. *Type 2:AI System Analyst Responsible for gathering and analyzing the requirements and contributing to designing, developing, testing, and deploying in all phases of the AI system development project lifecycle, as below: 1. Collaborate with business data analysts to analyze user requirements and ensure that requirements and acceptance criteria satisfy user needs. 2. Create a system requirement specification to elaborate on system design, system components/interfaces, and data schema. 3. Work with the multi-functional engineering team(e.g., data scientist, AI/ML engineer, data engineer, AI software engineer..., etc.) to ensure technical feasibility. 4. Drive usability and quality of the AI system developed and ensure all system requirements are well implemented. 5. Work on various enterprise AI system applications and problems, and analyze issues to identify root causes and prevention. 6. Create the Wireframes(or UI Prototypes) for AI system feature discussion with users and provide them to the UI/UX design team. 7. Author and update internal documentation; initiate and deliver requirement specification and system analysis/system design documentation. <Required Skills> 1. Practical experience in requirements gathering and analysis and change management 2. Experience co-working with front-end and back-end system development teams. 3. Strong analytical, verbal, and written communication, teamwork, and highly collaborative skills. 4. Experience with Microsoft Visio, Axure RP(or other wireframe tools). 5. Mastery of Microsoft Office programs (Word, Excel, PowerPoint) (Nice to have, but not necessary) 1. Mastery of QA methodology and problem-solving tools/techniques in software engineering. 2. Knowledge of AI/Data Science Tech(e.g., Machine Learning, Deep Learning, Statistics, Quantitative modeling techniques). 3. Practical experience in Relational DB (e.g., MS SQL, PostgreSQL) and ability to write SQL queries.
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