轉職熱搜工作

1. DT/ WKS硬體電路設計，使用CAD軟體開發電路圖。 2. 進行硬體原型測試，執行功率和性能基準測試，優化設計。 3. 從設計分析確認Design Quality,處理設計相關的問題，確保產品品質。 4. 診斷硬體故障，提供技術支援。 5. 新技術研究開發與導入

1. 結構強度模擬分析(DT/WS/NB等) 2. 支援結構性測試(衝擊/震動/落摔)送測-台灣 3. 支援熱傳溫度/噪音測試-台灣 4. 偕同不同function team合作進行驗證、分析與解決問題 5. 模擬新技術導入與研究 6. 模擬工具二次開發

1. 熱流模擬分析(DT/WS/NB等) 2. 熱傳溫度/噪音測試-台灣 3. 支援環境測試(溫濕度)送測-台灣 4. 偕同不同function team合作進行驗證、分析與解决問題 5. 模擬新技術導入與研究 6. 模擬工具二次開發

1. 結構強度模擬分析(Server/AI Server等) 2. 支援結構性測試(衝擊/震動/落摔)送測-台灣 3. 支援熱傳溫度/噪音測試-台灣 4. 偕同不同function team合作進行驗證、分析與解決問題 5. 模擬新技術導入與研究 6. 模擬工具二次開發

1. 環境測試(溫溼度)測試-台灣 2. 結構性測試(衝擊/震動/落摔)送測-台灣 3. 熱傳溫度測試-台灣 4. 噪音測試-台灣 5. 自動化測試導入-台灣 6. 廠內實驗室設備點檢/機台保養維護/待測機台與物料維護-台灣 7. 偕同不同function team合作進行驗證、分析與解決問題 8. 與第三方實驗室溝通協調、安排測試-台灣

＜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. At least 5 years of experience in embedded system, FreeRTOS is a plus 2. Familiar with C/C++, Linux, TCP/IP, Streaming 3. Familiar with Reatlek, OmniVision, Sigmastar, Rockchip, Ambarella, Hisilicon or any IP camera platform. 4. Excellent troubleshooting and problem-solving skills. 5. Well communicate with different team such as Hardware, Audio, Optical, IQ, DQA, customer, etc. 6. Familiar with Git control tool

1. Lead the lead of firmware team for server project. 2. Study technical issue for project‘s needs. 3. Review & sign off requirement specification. 4. Review engineer design. 5. Review function/interface specification. 6. Review bug fixing. 7. Help the design and review the common definitions for debugging and profiling.

1.Monitor applications in real time and handle abnormalities or errors. 2.Conduct troubleshooting, root cause analysis, and issue resolution to maintain system stability. 3.Support application optimization and small-scale development tasks. 4.Write and update maintenance process documentation, providing operational guidelines and best practices. 5.Monitor and maintain servers, networks, storage, and application systems to ensure high availability and stable operations. 6.Quickly diagnose, isolate, and resolve system and network issues, escalating incidents when necessary to minimize operational impact. 7.Perform scheduled maintenance, including system updates, patching, backups, and hardware checks. 8.Collaborate with IT, manufacturing, and engineering teams to align maintenance work with business objectives and drive improvement initiatives. 9.Document maintenance activities, incident handling, and solutions, generating reports for the day-shift team and management review. 10.Assist with system upgrades, new infrastructure deployments, and testing/monitoring of automated processes. 11.Participate in rotating day/night shifts to ensure 24/7 system availability. 12.Provide guidance and support to less-experienced team members when required.

Job Summary: The Statistician/Data Scientist will be responsible for all advanced statistical methods utilized for improving product quality, production yield, and factory effectiveness as well as finding the root-causes, along with other consulting services such as customer targeting and lead scoring in support of sales and marketing activities worldwide. Key Responsibilities: • Design, implement, and refine advanced statistical/Machine 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. • 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 predictive modeling techniques to improve decision-making to customers and stakeholders. • Collaborate effectively with team members, whether leading projects or supporting initiatives led by others. • Provide direction, training, and guidance to less experienced team members.