轉職熱搜工作

1. Manage team 2. BIOS development planning and management 3. Across function teams communication 4. Customer relationship maintain 5. Cultivate talents

1. 協助組裝硬體機構、安裝系統，並進行整機測試。 2. 協助至方案實施地點進行安裝、測試，並依據需求前往進行障礙排除(需要出差)。 3. 針對方案的零組件需求，進行規格設計、供應源搜尋、聯繫，並於採購成立後進行跟催。

Participated in the development of 5G/6G Integrated Sensing and Communication (ISAC) technologies, with responsibilities including: 1. Design and development of ISAC algorithms. 2. System integration and testing of ISAC platforms. 3. Development of ISAC application systems.

協助開發跨平台應用程式 (APP/Web) 與後端 API 服務；參與軟體專案的需求訪談、功能開發與規格文件彙整。配合執行 CI/CD 自動化整合測試與程式碼審閱，支援應用 AI 模型進行任務自動化之專案實踐，並學習撰寫高品質的技術佈署文件 (TDD)。 Assist in developing cross-platform apps and backend APIs. Participate in requirement gathering and technical specification compilation. Support CI/CD automated testing and code reviews. Collaborate on projects utilizing AI models for task automation and learn to author high-quality Technical Deployment Documents (TDD).

1.Develop and integrate software and firmware for unmanned vehicles such as UAVs and USVs. 2.Design and implement embedded firmware for sensor data acquisition, communication interfaces, and control logic. 3.Develop multi-sensor fusion algorithms (IMU, GPS, LiDAR, Camera) to enhance localization and navigation accuracy. 4.Collaborate with the autonomy and control algorithm teams for module integration and performance tuning. 5.Build system testing scripts and debugging tools; conduct software validation and field testing. 6.Prepare and maintain technical documentation to ensure reliability and scalability of the system. 7.Requirements communication: Communicate requirements with cross-departmental teams and report issues to the R&D department.

＜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.