Inc. AI & Machine Learning, Innovation Graduate Internship

Global Sports Jobs Limited; Pahokee (FL); On-site; USD 20,664,000 - 34,440,000; Part time

Benefits offered by this job

Relocation assistance

Job summary

Nike’s Advanced Product Intelligence internship sits within the Advanced Innovation Collective, applying AI/ML to accelerate product concept development across design, engineering, and data analysis. The intern collaborates with ML Engineering, Computational Engineering, Generative AI Design, and Data Analysis teams on intelligent models and prototypes.

The role is project-based with tailoring options, concluding with a stakeholder presentation and documentation to scale the work beyond summer.

Qualifications

• Pursuing a Master’s degree with graduation between Winter 2027 and Summer 2028.
• Strong foundational understanding of core machine learning concepts including supervised/unsupervised learning, neural networks, and model evaluation.
• Hands-on experience building AI/ML-powered systems or prototypes in academic, personal, or professional settings.
• Experience in surrogate modelling, computer vision, transformers, 3D geometry, or simulation data.
• Proficiency in Python and ML libraries (PyTorch, OpenCV, NumPy, pandas, TensorFlow, scikit-learn).

Responsibilities

• Apply AI/ML expertise to help teams explore, evaluate, and improve product concepts faster.
• Build intelligent models, workflows, and prototypes that use product performance data to accelerate innovation.
• Prototype AI/ML tools to support engineering and creative workflows for footwear and apparel design and simulation.
• Explore AI/ML applications in engineering simulation workflows including FEA, optimisation, and virtual product development.
• Train lightweight predictive ML models using various datasets.
• Build interactive prototypes or internal tools usable by Design and Engineering teams.
• Present the project to stakeholders at the end of the internship.
• Contribute to documentation to scale the work beyond the summer.

Skills

ML fundamentals

Python programming

Data analysis

Communication

Model evaluation

Education

Master’s degree in Computer Science, Machine Learning, Data Science, Computational Engineering, Mechanical Engineering, or related field

Tools

PyTorch

OpenCV

NumPy

pandas

TensorFlow

scikit-learn

Job description

About the Role

This internship sits within Nike’s Advanced Product Intelligence team, a group in the Advanced Innovation Collective that builds intelligent capabilities for designers, engineers, and creators to reimagine the future of physical product design. The intern works closely with multidisciplinary teammates across ML Engineering, Computational Engineering, Generative AI Design, and Data Analysis. The role applies AI/ML expertise to help teams explore, evaluate, and improve product concepts faster by building intelligent models, workflows, and prototypes that use product performance data. The internship is project-based, with room for tailoring based on the intern’s interests and strengths, and concludes with a project presentation to stakeholders.

Key Responsibilities

• Apply AI/ML expertise to help teams explore, evaluate, and improve product concepts faster.


• Build intelligent models, workflows, and prototypes that use product performance data to accelerate the product innovation process.


• Prototype AI/ML tools that support engineering and creative workflows for footwear and apparel design and simulation.


• Explore applications of AI/ML and product performance data to engineering simulation workflows, including physics-based modelling, FEA, optimisation, and virtual product development.


• Train lightweight, domain-specific predictive ML models using generated, simulated, experimental, or curated datasets.


• Build interactive prototypes or internal tools that make intelligent capabilities usable and understandable by Design and Engineering teammates.


• Present the project to stakeholders at the end of the internship.


• Contribute to documentation that helps scale the work beyond the summer.

Required Experience & Qualifications

• Currently pursuing a Master’s degree in Computer Science, Machine Learning, Data Science, Computational Engineering, Mechanical Engineering, or a related technical field, with a graduation date between Winter 2027 and Summer 2028.


• Strong foundational understanding of core machine learning concepts, such as supervised and unsupervised learning, neural network architectures, model training and evaluation, trade-offs in model selection, and overfitting and underfitting behaviour.


• Hands‑on experience building AI/ML-powered systems, workflows, or technical prototypes, whether academic, personal, or professional.


• Experience in one or more relevant areas, such as surrogate modelling, computer vision, transformers, 3D geometry, or simulation data.


• Exposure to engineering simulation methods such as finite element analysis (FEA), computational modelling, optimisation, or design exploration.


• Proficiency in Python and commonly used ML libraries, such as PyTorch, OpenCV, NumPy, pandas, TensorFlow, and scikit-learn.


• Comfort navigating ambiguous problem spaces and proposing own ideas or approaches.


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


• Graduate-level engineering intern with a strong foundation in machine learning and artificial intelligence, eager to apply those skills to real-world challenges in product innovation.

Preferred Skills

• Experience in computational engineering, mechanical engineering, simulation, digital product development, or physics-based modelling.


• Self-directed, builder mindset and curiosity for solving ambiguous and novel problems.


• Enthusiasm for digging into real-world datasets, training models, testing outputs, and improving them.


• Interest in both open-ended creative problems and well-defined engineering challenges.

Salary

• 10-week paid internship

Benefits & Perks

• Relocation assistance

The Application Process

Accommodations are available to complete the interview process, including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed.