轉職熱搜工作

1. 於 NVIDIA Isaac Sim / Omniverse 平台中建置高擬真物理模擬環境（包含地形、障礙物與感測器配置）。 2. 協助開發與訓練機械狗之 強化學習 (RL) 步態模型。 3. 協助 Sim2Real 參數調整與數據分析。

一、基本資格與學歷： 學歷：大學以上畢業，電機、土木、機械、能源或相關工程科系尤佳。 工作經驗：3年以上工程專案管理相關經驗，具備能源、電力或機電工程經驗尤佳。 二、專業技能要求： 熟悉工程專案管理流程，包括進度規劃、成本控制、品質管理、風險評估與現場督導等。 具備工程合約撰寫、審閱、執行與管控經驗。 熟悉專案管理軟體（如MS Project或Primavera）並具備專案時程與資源控管能力。 熟悉工程設計圖、施工圖及技術規範，具AutoCAD或其他工程設計軟體使用能力尤佳。 熟悉工程法規，如建築法、勞工安全衛生法、電業法、環保法令等相關規範。 三、工作經驗與實績要求： 曾完整執行過中大型工程專案管理（例如：儲能系統、太陽能發電、工業廠房建置）且具備具體專案實績說明尤佳。 曾協助或主導工程專案之預算編列、成本控管與採購合約管理經驗。 熟悉現場施工督導與協調管理，並具備解決工程問題與突發狀況之能力。 四、證照與專業資格要求（非必備但加分）： 具備PMP國際專案管理師證照尤佳。 具備乙級以上電匠證照或土木、機械工程相關專業證照尤佳。 五、能力與特質要求： 優秀的跨部門溝通、協調能力，能與業主、承包商、監造、設計團隊有效溝通。 邏輯清晰，具備良好的計畫能力與執行力，能主動提出問題並有效解決。 能抗壓且具備高度責任感及職業道德，能適應短期出差或駐工地需求。 六、其他加分條件： 曾參與能源或電力相關專案（如太陽能、儲能、智慧電網）規劃與執行者尤佳。 具備跨國企業或跨區域工程專案管理經驗尤佳。

1. Conduct data collection, preprocessing, analysis, feature engineering, AI development, and deploy AI application services. 2. Utilize machine learning, generative AI, and data analytics technologies to design AI models for specific applications (e.g., robotics, healthcare, cybersecurity, energy, etc.) using unstructured data (e.g., videos, images, log data, human behavior, network traffic packets, etc.). 3. Optimize and integrate distributed training and large-scale AI model optimization tools and frameworks (e.g., Horovod, FairScale, DeepSpeed, etc.). 4. Collaborate with cross-functional teams to understand business requirements and propose solutions.

Develop AI imaging technology for smart robots and robotic dogs, participating in one or more of the following technology developments: 1. VLM (Vision-Language Model) navigation and LBM (Large Behavior Model) integration. 2. End-to-end vision-based 3D environmental perception. 3. Real-time image tracking and terrain analysis. 4. Multi-robot collaboration, formation control, and swarm intelligence. 5. Validation using the Omniverse simulation platform. 6. Integration and development of physical robots and robotic dogs.

1. Research and development of multi-modal perception fusion algorithm software. 2. Machine learning/machine vision, and AI/GAI/Agentic AI algorithm research and development.

工作內容 1. 負責台灣北區客戶的維運與開發，聚焦醫療、工業自動化、半導體及機器人等應用領域。 2. 以馬達產品為核心，延伸至驅動模組整合與系統應用，依據客戶需求規劃最適化的馬達、減速機與控制組合，並提供整體解決方案與技術建議。 3. 跨部門協調應用工程／FAE 團隊，執行技術討論、規格確認與商務談判，確保專案時程順利推進並成功導入量產。 4. 追蹤專案進度與財務績效，定期彙整銷售預測、市場分析及競品動態報告。 5. 參與國內外展會與產業研討會，拓展新客戶、通路與策略合作夥伴。

＜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.Development of machine vision correction algorithm 2.2D/3D optical detection algorithm development 3.Development of SLAM image computing algorithm 4.GAI/AI model development 5. Machine vision system integration