Open to remote work except in South Dakota, Vermont and West Virginia.

Information about benefits can be found here.
 

Who are we looking for

We are actively seeking to hire multiple Machine Learning Engineers to join our AI/ML team. As a Machine Learning Engineer within the AI/ML team, you will be developing advanced analytics systems that directly impact our business. You will work on a cross-disciplinary team (data/API/infra/infosec/ML) to enable data-driven decision making across multiple organizations.

Working at the intersection of machine learning and software engineering (i.e., MLOps), you’ll create high-quality solutions that power Nike. You will work with others who are energized by the challenge of building things from the ground up, thinking out of the box, and applying the latest technologies in statistical, unsupervised, supervised, and machine learning models at global scale.

Our teams enjoy a collaborative and academic environment that promotes developing new skills, mentorship, and a drive to deliver knowledge and software back to analytics and engineering communities, within and Nike and without. This culture is cultivated by intellectual curiosity, fun, openness, and diversity.

Sound like you?

Who will you work with

AI/ML is one of the key groups within Data and Analytics. We’re chartered to scale machine learning and AI at Nike. For areas of the business early in their analytics journey, we embed cross-disciplinary teams of data scientists and engineers to unlock new capabilities and answer unsolved (or unasked!) questions.

In mature areas with preexisting data science teams, we help scale machine learning by attaching engineering squads to grow their capacity to deliver for the business. In addition, we collaborate closely with platform and architecture partners to develop capabilities that simplify machine learning at scale within Nike (e.g., model management, A/B testing, feature stores).

Essential Job Functions:

• Serve as an integral member of a cross-functional engineering teams that delivers solutions unlocking machine learning for Nike. You will analyze and profile data to uncover insights in support of scalable solutions, clean, prepare and verify the integrity of data for analysis and model creation. You’ll also track model accuracy, performance, relevance, and reliability. You will apply a variety of machine learning and collaborative filtering methods to data sets. You will aid in building APIs and software libraries that support adoption of models in production
• Leverage your prior experience, knowledge of industry trends, and personal creativity to develop new and innovative solutions which delight our customers in their mission to serve Athletes*.
• Stay current with industry trends and recommend relevant technologies & products in the areas of Analytics, Machine Learning, Artificial Intelligence, and Data Science tools and other emerging technologies. Given the rapid pace of change in technology and machine learning today, always be pushing the boundary of what’s possible and be on the offense always.
• Embrace and embody Nike’s core values (maxims) in your work and interactions with peers and stakeholders. Communicate effectively, building trust and strong relationships across the company, do the right thing.

What you bring to Nike

• Bachelor’s degree in a relevant field, like computer science or software engineering, or equivalent experience.
• Understanding of Machine Learning, its applications, and the lifecycle of an ML application in production; including an ability to articulate the role of MLOps in model development from experimentation to production and measurement
• An ability to meaningfully communicate, written, orally, and visually technical topics with peers and articulate the benefits and tradeoffs of various solutions
• Experience working in and/or collaborating with a partial or fully distributed team
• Understanding of data structures, algorithms, and data solutions
• Experience in applying Python (or another language commonly used in the field of ML, such as Scala, Julia, C++) and SQL to ML and/or software and data engineering tasks
• Familiarity with ETL, ML, or analytics technologies such as Scikit-learn, Pandas, OpenCV, NumPy, TensorFlow, or similar platforms and frameworks
• Awareness of data science platforms (like Databricks or SageMaker), distributed engines (like Spark and AWS cloud), and CI/CD pipelines and containerization are preferred
• Fluency in the application of open-source technologies and the potential of standardized platforms in the area of Data Science, AI, & ML.
• Nice to have: an interest in the potential of Generative AI to accelerate common development and data science tasks, or the deployment of Generative AI solutions in the enterprise