Platform Power Thermal Performance Engineer

Qualifications

• This role is designed for graduate candidates who want to grow into platform performance engineers.
• You will have opportunities to develop practical expertise in AI server architecture, CPU host performance, GPU I/O behavior, benchmarking methodology, and at-scale platform optimization concepts.
• We value strong technical fundamentals, curiosity, hands-on learning ability, and clear communication. 1.
• Solid fundamentals in computer architecture, operating systems, or computer systems, with interest in CPU, memory, and I/O subsystem behavior. 2.
• Basic understanding of GPU computing, parallel programming, or accelerator-based systems; related coursework, research, or project experience is a plus. 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.