AI Software Intern: Build & Deploy ML Models

Global Foundries; Santa Clara (CA); In loco; USD 28.000 - 55.000; Tempo pieno

Descrizione del lavoro

GlobalFoundries is seeking a curious and driven AI Software Intern to bridge cutting-edge machine learning research and scalable software production. Working alongside senior software engineers, the intern will build, optimize, test, and deploy AI models within an AI platform while gaining hands‑on exposure to the AI inference software lifecycle and AI processor architectures.

You will analyze latency, memory usage, and computational footprint, and help optimize AI inference workflows for edge

Competenze

• Sophomore student pursuing a degree in Computer Science, Computer Engineering or Data Science.
• Familiarity with PyTorch or TensorFlow required.
• Strong object-oriented programming and version control skills.
• English fluency (written & verbal) required.
• Willingness to work 40 hours per week during internship.

Mansioni

• Assist with integrating, deploying, testing, and scaling ML models within production AI pipelines.
• Analyze latency, memory usage, and computational footprint; optimize AI inference workflows.
• Use modern AI-assisted tools to accelerate prototyping, code development, testing, and debugging.
• Document designs, experiment configurations, and performance benchmarks for clear reproducibility.

Conoscenze

PyTorch

TensorFlow

OO programming

Git version control

English fluency

40 hours/week

Formazione

Sophomore status; pursuing Bachelor's, Master's, or PhD in CS/CE/Data Science

Minimum 3.0 GPA

Strumenti

GitLab

GitHub

Descrizione del lavoro

GlobalFoundries is seeking a curious and driven AI Software Intern to bridge cutting-edge machine learning research and scalable software production. Working alongside senior software engineers, the intern will build, optimize, test, and deploy AI models within an AI platform while gaining hands‑on exposure to the AI inference software lifecycle and AI processor architectures.

You will analyze latency, memory usage, and computational footprint, and help optimize AI inference workflows for edge