Job Details:
We are looking for motivated graduate candidates who are interested in platform performance engineering for AI server platforms. In this role, you will learn and contribute to GPU-as-an-I/O-device performance validation and optimization, working across CPU host architecture, I/O subsystems, GPU device interactions, and server-level performance analysis. You will work with experienced engineers to develop validation methodologies, execute performance experiments, analyze results, and support technical issue investigation across reference designs and customer-oriented systems. This role provides a strong opportunity to build hands-on expertise in modern AI infrastructure, from single-server performance tuning to cluster-scale design concepts.
Roles and Responsibilities
• Learn and contribute to topology validation and performance analysis for AI server platforms, covering CPU host, GPU, memory, storage, and network interactions.
• Support validation plan development, benchmark execution, data collection, and result analysis for CPU host and GPU I/O performance studies.
• Work with senior engineers to debug performance issues, summarize findings, and propose practical improvement ideas from the host platform perspective.
• Collaborate with cross-functional teams to communicate test progress, document technical observations, and support issue closure under guidance.
Requirements
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.
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.
Interest in AI server and cluster-level system design, including GPU scale-up, server scale-out, networking, storage, and performance bottleneck analysis.
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.
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.
Position of Trust
N/A
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.
Nice to have
2. Basic understanding of GPU computing, parallel programming, or accelerator-based systems; related coursework, research, or project experience is
Solid understanding of computer architecture and system fundamentals; exposure to concepts such as PCIe/CXL, coherency, IOMMU, NUMA, or host-device data movement is
Basic knowledge of GPU architecture, GPU programming, or AI accelerator software stacks; experience with CUDA, ROCm, OpenCL, SYCL, or related frameworks is
Familiarity with benchmark methodology, performance metrics, and experiment design; hands-on lab, coursework, internship, or research project experience is
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