Senior Machine Learning Engineer – AI/ML (Remote Work Option)

Company: Nike

Location: Remote (Excluding South Dakota, Vermont, and West Virginia)

Salary Information

The annual base salary for this position ranges from $99,500.00 in our lowest geographic market to $222,900.00 in our highest geographic market. The actual salary will vary based on a candidate's location, qualifications, skills, and experience. Details about our benefits can be found .

Job Overview

We are actively seeking to hire multiple Senior Machine Learning Engineers to join our AI/ML team. As a Sr. 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'll collaborate with other highly motivated professionals to build things from the ground up, thinking outside the box, and employing the latest technologies in statistical, unsupervised, supervised, and machine learning models on a global scale.

Our teams enjoy a collaborative and academic environment that supports new skill development, mentorship, and sharing knowledge and software back to analytics and engineering communities within and outside of Nike. This culture is cultivated by intellectual curiosity, fun, openness, and diversity.

Who Will You Work With?

The AI/ML team is a key group within Data and Analytics at Nike. We’re chartered to scale machine learning and AI company-wide. We embed cross-disciplinary teams of data scientists and engineers to unlock new capabilities and answer previously unsolved (or unasked) questions in business areas early in their analytics journey.

In mature business areas with pre-existing data science teams, we help scale machine learning by attaching engineering squads to grow their capacity to deliver for the business. Additionally, we closely collaborate 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).

Key Responsibilities

• Serve as an integral member of a multi-functional engineering team delivering solutions that unlock machine learning for Nike.
• Analyze and profile data to uncover insights in support of scalable solutions, clean, prepare, and verify data integrity for analysis and model creation.
• Track model accuracy, performance, relevance, and reliability.
• Apply various machine learning and collaborative filtering methods to data sets.
• Aid in building APIs and software libraries supporting model adoption in production.
• Leverage industry trends and personal creativity to develop innovative solutions, delighting our customers in their mission to serve Athletes*.
• Stay current with industry trends and recommend relevant technologies in Analytics, Machine Learning, Artificial Intelligence, and Data Science.
• Embrace and embody Nike’s core values (Maxims) in your work and interactions with peers and stakeholders. Communicate effectively, build trust, and maintain strong relationships across the company.

Qualifications

• 3+ years of experience in ML Engineering or Software Engineering with a relevant bachelor’s degree, or equivalent experience.
• Understanding of Machine Learning and its lifecycle, including the role of MLOps in model development from experimentation to production and measurement.
• Ability to communicate technical topics clearly in written, oral, and visual forms.
• Experience working in or collaborating with a partially or fully distributed team.
• Strong understanding of data structures, algorithms, and data solutions.
• Experience applying Python (or similar languages such as Scala, Julia, C++) and SQL to ML, software, and data engineering tasks.
• Familiarity with ETL, ML, or analytics technologies, such as Scikit-learn, Dask, TensorFlow, Kubeflow, Spark, EMR, or similar platforms.
• Preferred awareness of data science platforms (like Databricks or SageMaker), distributed engines (like Spark and AWS Cloud), and CI/CD pipelines and containerization.
• Fluency in the application of open-source technologies and standardized platforms in Data Science, AI, & ML.