NIKE, Inc. Innovation AI & Machine Learning Graduate Intern

Job Description

WHO YOU’LL WORK WITH

This internship is part of Nike’s Advanced Product Intelligence team, a group within the Advanced Innovation Collective, that focuses on building intelligent capabilities for designers, engineers, and creators to reimagine the future of physical product design. During your internship, you’ll work closely with multidisciplinary teammates across Machine Learning Engineering, Computational Engineering, Generative AI Design, and Data Analysis.

WHAT YOU’LL WORK ON

In the role of an Innovation AI & Machine Learning Engineer Intern, you will build intelligent models & workflows that assist and accelerate the product innovation process. You will lead or collaborate on efforts such as :

Prototyping AI / ML tools that support creative workflows for footwear and apparel design

Fine-tuning image diffusion, vision-language, and generative 3D models geared towards product design exploration

Training lightweight, domain-specific predictive ML models from generated or curated datasets

Building interactive prototypes or internal tools that make intelligent capabilities readily available for use by Design and Engineering teammates

Over the course of the internship, you’ll work alongside a passionate and multidisciplinary team that’s shaping the future of product innovation at Nike. You’ll present your project to stakeholders at the end of the internship and contribute to documentation that helps scale your work beyond the summer. And finally, you’ll walk away with meaningful, real-world experience at the intersection of AI & Machine Learning, furthering breakthrough products that make athletes

• better.

WHO WE ARE LOOKING FOR

We’re looking for a graduate-level engineering intern with a strong foundation in machine learning and artificial intelligence who is eager to apply those skills to real-world challenges in product innovation.

This internship will be project-based, with room for tailoring based on your interests and strengths. Our ideal candidate brings not only technical capability, but also a self-directed, builder mindset and a curiosity for solving ambiguous and novel problems. You love digging into real-world datasets, training models, testing outputs—and improving them. You’re as excited by open-ended creative problems as you are by well-defined engineering challenges.

Currently pursuing a Master’s degree in Computer Science, Machine Learning, Data Science, Computational Engineering, or a related technical field with a graduation date between Winter 2026 and Summer 2027

Strong foundational understanding of core machine learning concepts (e.g., supervised & unsupervised learning, neural network architectures, model training and evaluation, tradeoffs in model selection, overfitting and underfitting behavior)

Experience working with and applying diffusion models, computer vision models, transformers, and / or 3D geometry data

Hands-on experience with fine-tuning Generative AI image diffusion models and integrating them into creative workflows

Hands-on experience building AI / ML-powered systems or prototypes (either academic, personal, or professional)

Proficiency in Python and commonly used ML libraries (e.g., PyTorch, OpenCV, NumPy, pandas, TensorFlow, scikit-learn)

Comfort navigating ambiguous problem spaces and proposing your own ideas or approaches

Demonstrated ability to communicate technical findings to diverse audiences in verbal and written form

This internship – as well as full-time positions – are located in-person at the NIKE, Inc. World Headquarters in Beaverton, OR.  Check out this video of our World Headquarters to learn more about life on campus :  Nike WHQ Campus Video

We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form.