WHO ARE WE LOOKING FOR

As a Machine Learning Engineer within the Data & Artificial Intelligence (D&AI) organization, you will be developing sophisticated analytics systems that directly impact our business. You will get a chance to work on a cross-disciplinary team (DevOps/Data/Software Engineering) to enable data-driven decision-making across multiple projects.

WHAT WILL YOU WORK ON

Working at the intersection of machine learning and software engineering, you’ll create high-quality solutions that power Nike. Our AI/ML team is a collaborative and academic environment that promotes intellectual curiosity, diversity, and a drive to deliver knowledge and software back to the analytics and engineering communities. We’re a global organization with teammates in various time zones, working to solve machine learning problems at scale.

As a member of our team, you’ll design and implement scalable applications that leverage prediction models and optimization programs to deliver data-driven decisions with immense business impact. You’ll contribute to core advanced analytics and machine learning platforms and tools, and thrive in an environment where talented colleagues share knowledge and skills.

We value and nurture our culture by seeking to always be collaborative, curious, fun, open, and diverse. If you’re passionate about learning, contributing to the analytics and engineering communities, and working in a dynamic environment, we’d love to hear from you.

WHO WILL YOU WORK WITH

In this role, you’ll be working closely with the rest of our global team, along with commercial and consumer analytics, and enterprise architecture teams.

WHAT YOU BRING

• Bachelor’s degree in computer science or a related field, or equivalent combination of education and experience.
• 1-3 years of professional experience in machine learning or a related field, with a strong foundation in software development.
• In-depth understanding of machine learning concepts, applications, and the lifecycle of an ML application in production, including the role of MLOps in the development lifecycle.
• Proven analytical and problem-solving skills, with the ability to break down complex problems into manageable components.
• Proficiency in writing clean, maintainable, and scalable code in Python.
• Solid grasp of computer science fundamentals, including data structures, algorithms, data modeling, and software architectures.
• Excellent communication skills, with the ability to effectively collaborate with team members and stakeholders, and communicate technical ideas through code and documentation.
• Experience with technologies such as PyTorch, Spark, Docker, Jenkins, Airflow, and Databricks.
• Familiarity with MLOps and API development principles.
• Experience with cloud architecture and technologies, particularly Amazon Web Services.

Nice to Have:

• Knowledge or hands-on experience with deep learning, recommender systems, NLP and their applications.
• Experience with data engineering concepts, including data sets, ETL pipelines, SQL, and data warehousing.
• Understanding of agile development methodologies and test-driven development paradigms.
• Interest in exploring the potential of Generative AI to accelerate development and data science tasks or deploying Generative AI solutions in an enterprise setting.