3.
Interest in AI infrastructure and at-scale system concepts, including server architecture, scale-up/scale-out design, networking, and storage.
Candidates should be upcoming graduates with a Master of Science degree, or higher, in Electrical Engineering, Computer Science, Computer Engineering, or a related technical field. The ideal candidate is passionate about learning new technologies, solving complex system problems, and improving validation efficiency through structured analysis and automation.
• Solid understanding of computer architecture and system fundamentals; exposure to concepts such as PCIe/CXL, coherency, IOMMU, NUMA, or host-device data movement is a plus.
• Basic knowledge of GPU architecture, GPU programming, or AI accelerator software stacks; experience with CUDA, ROCm, OpenCL, SYCL, or related frameworks is a plus.
• Interest in AI server and cluster-level system design, including GPU scale-up, server scale-out, networking, storage, and performance bottleneck analysis.
• Familiarity with benchmark methodology, performance metrics, and experiment design; hands-on lab, coursework, internship, or research project experience is preferred.
• Good programming and scripting skills, such as Python, C/C++, or shell scripting, with the ability to automate tests, process data, and support performance analysis.
• Experience in system validation, performance testing, Linux-based development, machine learning/deep learning workloads, networking, storage, or GPU computing through internship, research, or academic projects is a plus.
• Strong learning agility, problem-solving mindset, teamwork, and ability to work in a fast-changing technical environment.
• Good verbal and written English communication skills, with the ability to document technical findings clearly.