*CABG-BMC韌體研發工程師(土城)

1. 負責 ROS2 軟體模組開發。 

2. 負責導航系統功能開發與維護。 

3. 負責 Localization、Path Planning、Obstacle Avoidance 等功能優化。 

4. 整合 LiDAR、Camera、IMU 等感測器。 

5. 整合第三方設備與外部系統。 

6. 分析系統異常與效能瓶頸。 

7. 執行效能調校與穩定度優化。 

8. 執行場域驗證與壓力測試。 

9.建立測試案例與驗證流程。

1.負責機器人軟硬體系統架構規劃與設計。 

2.建立 ROS2 軟體平台與跨模組整合框架。 

3.規劃 Navigation、Manipulation、Perception、Agent 之間的協作機制。 

4.推動 NVIDIA Isaac Ecosystem 導入與技術驗證。 

5.建立 VLA、World Model、Digital Twin 技術路線。 

6.建立 Sim2Real / Real2Sim 工作流程。 

7.與產品、演算法及場域團隊共同制定技術 Roadmap。

＜About the Job＞ 

Working on top of hardware provided by robot vendors, you will be responsible for integrating our internal AI vision and decision models into real-world robotic systems. You are the technical implementation core that connects the 〝AI Brain〝, 〝Simulation & Validation〝, and 〝Physical Hardware〝 layers. Your mission is to bridge the gap between AI models and the physical world, ensuring our robotic systems operate reliably in industrial environments. You will operate with a high degree of autonomy, collaborate as an equal partner with the AI modeling team, and own the end-to-end success of the integration layer. 

＜Job Responsibilities＞ 

1.AI model integration & motion control 

．Receive high-level action commands from AI models and perform kinematic solving and trajectory planning. 

．Implement collision detection and safety mechanisms to ensure AI-generated actions execute reliably in real industrial environments. 

．Maintain spatial calibration and coordinate consistency between visual inputs and action outputs. 

2. Vendor interface wrapping & control implementation 

．Rapidly interpret and integrate various interfaces and SDKs provided by robot vendors. 

．Encapsulate proprietary vendor APIs into standardized interfaces consumable by internal systems. 

．Manage communication between edge computing devices and robot controllers, resolving stability issues encountered during real-machine testing. 

3. Simulation validation & real-world deployment 

．Build robot simulation environments for industrial scenarios and complete stress tests and edge-case validations before deploying to physical machines. 

．Establish AI model inference pipelines on edge devices and continuously optimize end-to-end performance. 

4. On-site debugging & exception handling 

．Conduct real-machine testing on-site, systematically collecting failure cases and data to feed back to the AI modeling team for continuous improvement. 

．Implement state machines and exception handling logic to ensure safe degradation and recovery in uncertain situations. 

＜Minimum Qualifications＞： 

．5+ years of experience in robotics systems, automation equipment, or hardware/software integration. 

．Solid robotics middleware development skills, with hands-on experience integrating third-party SDKs and hardware interfaces. 

．Proven experience in real-machine deployment or on-site testing; able to independently diagnose and resolve hardware communication issues. 

．Proficient in Python and C++; able to rapidly produce production-ready code within an existing architecture. 

．Solid understanding of Computer Vision and 3D spatial geometry, with practical experience in sensor calibration and coordinate transformations. 

．Strong independent troubleshooting skills in Linux and Docker environments.

1. 開發與維護自動化測試框架核心模組 

2. 設計硬體通訊介面，整合多種設備連接協定 

3. 建構分散式任務排程與執行系統 

4. 對接外部工廠資訊系統（SFC/MES）API 

5. 維護管理介面與開發者工具

1. 設計與建置 HPC / AI 叢集高速網路架構（Spine-Leaf、Fat-Tree 等大規模拓撲） 

2. 具備 InfiniBand（IB）或 RoCE / RDMA 高速網路實務經驗 

3. 建構網路自動化工具與配置管理流程，降低人工操作風險 

4. 執行網路設備驗測與效能壓測，確保 AI 訓練工作負載的穩定性與吞吐量 

5. 診斷並排除複雜網路問題，建立故障根因分析（RCA）機制

1. Windows / Android APP / Linux 與驅動程式開發 

2. Server 測試工具與自動化程式開發 

3. Server Diag. integration / validation

1.負責Server安規相關產品設計、開發等工作，對線路原理圖、結構設計圖等進行評審，確保結構硬件設備符合安規能耗標準，降低安規風險。 

2.負責Server產品的安規能耗測試工作，制定產品測試計劃，完成產品安規能耗的測試及測試報告。 

3.負責Server產品安規問題整改，優化安規設計方案，保證產品項目順利進行。 

4.負責Server產品安規能耗環境以及國際轉證的相關認證工作。

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.Develop and maintain BMC firmware for AI servers, including OpenBMC platform bring-up and feature development (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.

We are seeking a highly motivated Software Team Leader, who combines technical leadership, system-level thinking, and cross-functional collaboration with R&D, Operations, and global customers to enable efficient diagnostics, automation, and intelligent data integration. 

Responsibilities 

•Lead the software team to design, develop, and integrate diagnostic software solutions for AI server and storage platforms. 

•Build a unified Diagnostics OS that supports modular and reusable test components across multiple products and customers. 

•Drive automation, standardization, and integration with production lines. 

•Collaborate with R&D and Operations to enable efficient issue diagnosis and resolution throughout the product lifecycle. 

•Develop and maintain automation tools, test scripts, and diagnostic libraries for manufacturing. 

•Define and enforce standard interfaces and log formats for seamless integration with the operation flow. 

•Document diagnostic procedures, APIs, and best practices. 

•Evaluate and adopt new tools or technologies to enhance testing efficiency and data analytics. 

Qualifications 

• Strong communication and problem-solving skills, able to respond quickly to customer and factory issues from different time zones. 

• Solid understanding of server system, AI server architecture, and diagnostics workflows. 

• Experience leading software teams and coordinating with cross-functional teams for global customers. 

• Proficient in software development lifecycle management and quality assurance practices.

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

- 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

•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