AI GPU Arch Perf Analysis Intern
Intel
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What analysis roles keep asking for: Python (22%), Excel (18%), Accounting (16%), SQL (13%) — counted across their open postings here.
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Questions you are likely to be asked
- Why do you want to join Intel?
- What is your experience with Python? Tell me one thing you learned the hard way.
- How did you know your model was actually good, and not just good on your test set?
- Tell me about a time the data was messy or wrong. What did you do?
- How would you explain your model's result to someone who is not technical?
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Practise the AI GPU Arch Perf Analysis Intern at Intel interview free →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.
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Listed on workday · posted 2026-10-09. ApplySarthi collects openings and links to application pages; the role is advertised by Intel, not by us.