AI Infrastructure Architect Intern: Clusters & AI Systems

Intel Corporation

Santa Clara (CA)

On-site

USD 133,000 - 134,000

Full time

6 days ago

Be an early applicant

Application generator

Turn this role into an interview — a resume and cover letter built around what this employer wants.

Get past ATS filters

Job summary

Intel Corporation in Santa Clara, CA is seeking an AI Infrastructure Solution Architect Intern to join our AI Platforms team. You will work with experienced engineers on AI accelerator platforms, multi‑node clusters, system validation, and AI workload benchmarking.

This internship offers hands‑on experience across AI infrastructure, system software, cluster operations, firmware debugging, and end‑to‑end AI solution development, with emphasis on AI systems, Linux, and large‑scale computing

Qualifications

Currently pursuing Master's in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or AI/ML or related fields.
Experience with Python programming.
Familiarity with Linux command line and system administration concepts.
Understanding of computer architecture, operating systems, networking, or distributed systems.
Demonstrated ability to solve complex engineering or technical problems through coursework, research, personal projects, robotics competitions, hackathons, open‑source contributions, or previous internships.
Strong analytical and problem‑solving skills.
Ability to work independently and collaborate in a team environment.

Responsibilities

AI Infrastructure and Cluster Operations: Assist in deploying and validating AI accelerator systems and multi-node AI clusters. Support cluster bring‑up activities, including system provisioning, software installation, and health verification.
System Debugging and Validation: Analyze hardware, firmware, driver, and software logs to identify failures. Assist with validation of BIOS, firmware, and system software configurations.
AI/ML Workloads: Deploy and evaluate AI models using frameworks such as PyTorch and vLLM. Benchmark AI workloads and identify performance bottlenecks.
Automation and Tool Development: Develop Python‑based tools to automate testing, monitoring, and data collection. Create dashboards, scripts, and utilities for cluster management and diagnostics.
End‑to‑End AI Solutions: Participate in building end‑to‑end AI solutions, such as generative AI, agentic AI, computer vision, robotics, or intelligent automation projects.