AI GPU Arch Perf Analysis Intern

Intel is hiring an AI GPU Arch Perf Analysis Intern in Beijing, China. Level rates it ; you can apply on Intel.

Level's read on this job

Level rates this role Builds AI ●●●●: the job is building AI systems.
Level classifies it as an internship.
Level lists 864 other open roles at Intel.
The role was posted in the last few days.

AI in this role

Job Details:

Job Description:

As an AI Architecture Performance Analysis Graduate Intern, you will join Intel's GPU Compute Architecture team and contribute to core GPU kernel perf analysis using real AI workloads. Your work will directly support hardware/software co design and help analyze and shape the performance of next generation Intel GPU and AI accelerator platforms, while giving you hands on exposure to GPU architecture and low level performance engineering. Key Responsibilities • Analyze and optimize core GPU compute kernels for AI and numerical workloads (e.g., GEMM, Attention, operator fusion). • Reproduce representative AI inference and training workloads for GPU IP validation. • Perform GPU performance profiling and analysis to identify compute, memory, and pipeline bottlenecks. • Build performance profiles and models to understand architecture level performance behavior. • Provide workload and kernel level insights to support GPU architecture design and HW/SW co design efforts.

Qualifications:

Minimum Qualifications • Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field. • Proficiency in Python for analysis, experimentation, or tooling. • Solid understanding of AI fundamentals, including common models and algorithms. • Strong interest in GPU architecture, GPU programming, parallel computing, and performance optimization. • Basic knowledge of computer systems, such as CPU/GPU architecture, memory systems, and performance analysis. Preferred Qualifications • Experience with GPU kernels or programming models (e.g., CUDA, OpenCL, SYCL, Triton). • Exposure to performance optimization, compiler, or parallel computing coursework, research, or internships. • Strong analytical and problem solving skills, with the ability to reason from profiling data. • Interest in AI systems and infrastructure, beyond model level development. • Ability to work effectively in a collaborative, cross functional engineering environment.

Job Type:

Student / Intern

Shift:

Shift 1 (China)

Primary Location:

PRC, Beijing

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.

How we rate this

AI GPU Arch Perf Analysis Intern at Intel rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.