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.
Job Type
College Grad
Shift
Shift 1 (China)
Primary Location
PRC, Shanghai
Additional Locations
Posting Statement
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
- Work Model for this Role
- This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.
ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.
