AI GPU Architecture and Performance Optimization Intern

Job Title

AI GPU Architecture and Performance Optimization Intern

Role Summary

Join the GPU Compute Architecture team to optimize core GPU compute kernels and validate GPU IP using representative AI workloads. Work focuses on performance engineering to support hardware/software co-design and shape performance of future Intel GPU and AI accelerator platforms.

This is a hands-on internship involving profiling, analysis, and building performance models to inform architecture and design decisions.

Experience Level

Internship — entry-level (student intern). No prior professional experience required beyond relevant coursework, research, or project work.

Responsibilities

Primary responsibilities include optimizing kernels, reproducing workloads for validation, and providing data-driven insights to architecture teams.

• 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.

Requirements

Summary of must-have and preferred technical skills. Education details are captured under Education Requirements below.

• Must-have: Proficiency in Python for analysis, experimentation, or tooling.
• Must-have: Solid understanding of AI fundamentals, common models, and algorithms.
• Must-have: Basic knowledge of computer systems, including CPU/GPU architecture, memory systems, and performance analysis.
• Must-have: Strong interest in GPU architecture, GPU programming, parallel computing, and performance optimization.
• Must-have: Strong analytical and problem-solving skills; ability to reason from profiling data.
• Nice-to-have: Experience with GPU kernels or programming models (CUDA, OpenCL, SYCL, Triton).
• Nice-to-have: Exposure to performance optimization, compilers, or parallel computing through coursework, research, or internships.
• Nice-to-have: Interest in AI systems and infrastructure beyond model-level development; ability to work in cross-functional teams.

Education Requirements

Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field (student status required for internship).

About the Company

Company: Intel Corporation

Headquarters: Santa Clara, California, USA

Intel Corporation is a leading multinational technology company known for its innovative semiconductor solutions, including microprocessors, artificial intelligence accelerators, and memory products. Headquartered in the United States, Intel focuses on cutting-edge technology and a collaborative working environment, driving advancements in semiconductor manufacturing to meet global demands. The company emphasizes professional development and aims to shape the future of technology through groundbreaking designs.