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About This Role
Position Type Full Time Career area Business Operations Location 1 Bowerman Drive, Beaverton, Oregon 97005, United States Job ID R-94412* Become a Part of the NIKE, Inc. Team
NIKE, Inc. does more than outfit the world’s best athletes. It is a place to explore potential, obliterate boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. The brand seeks achievers, leaders and visionaries. At NIKE, Inc. it’s about each person bringing skills and passion to a challenging and constantly evolving game.
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 ML Engineering, Computational Engineering, Generative AI Design, and Data Analysis.
WHAT YOU’LL WORK ON
In the role of an AI/ML Engineer Intern, you will apply your AI/ML expertise to help teams explore, evaluate, and improve product concepts faster. You will build intelligent models, workflows, and prototypes that use product performance data to accelerate the product innovation process. You will lead or collaborate on efforts such as:
- Prototyping AI/ML tools that support engineering and creative workflows for footwear and apparel design and simulation
- Exploring applications of AI/ML and product performance data to engineering simulation workflows, including physics-based modeling, FEA, optimization, and virtual product development
- Training lightweight, domain-specific predictive ML models using generated, simulated, experimental, or curated datasets
- Building interactive prototypes or internal tools that make intelligent capabilities usable and understandable 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/ML and 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. Candidates with experience in computational engineering, mechanical engineering, simulation, digital product development, or physics-based modeling are especially encouraged to apply.
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, 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 (e.g., supervised & unsupervised learning, neural network architectures, model training and evaluation, tradeoffs in model selection, overfitting and underfitting behavior)
- Hands-on experience building AI/ML-powered systems, workflows, or technical prototypes (either academic, personal, or professional)
- Experience in one or more relevant areas, such as surrogate modeling, computer vision, transformers, 3D geometry, or simulation data
- Exposure to engineering simulation methods such as finite element analysis (FEA), computational modeling, optimization, or design exploration
- 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 is a 10-week paid internship opportunity with relocation assistance. Curious to know what it’s like to be part of a world-class Internship Program? Check out our recap video from last summer: Nike Internship Program Recap 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.
NIKE, Inc. is a growth company that looks for team members to grow with it. Nike offers a generous total rewards package, casual work environment, a diverse and inclusive culture, and an electric atmosphere for professional development. No matter the location, or the role, every Nike employee shares one galvanizing mission: To bring inspiration and innovation to every athlete* in the world.
NIKE, Inc. is an equal opportunity employer. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability.
* * What You Can Expect OUR HIRING GAME PLAN
01 Apply
Our teams are made up of diverse skillsets, knowledge bases, inputs, ideas and backgrounds. We want you to find your fit – review job descriptions, departments and teams to discover the role for you.
02 Meet a Recruiter or Take an Assessment
If selected for a corporate role, a recruiter will reach out to start your interview process and be your main contact throughout the process. For retail roles, you’ll complete an interactive assessment that includes a chat and quizzes and takes about 10-20 minutes to complete. No matter the role, we want to learn about you – the whole you – so don’t shy away from how you approach world-class service and what makes you unique.
03 Interview
Go into this stage confident by doing your research, understanding what we are looking for and being prepared for questions that are set up to learn more about you, and your background.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At NIKE, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
NIKE AI Hiring
NIKE has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Beaverton, OR, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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