Entry ML Engineer

5 days ago

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Nike

WebsiteLinkedInAll Job Openings

Athletic Shoes • Apparel • Sports Equipment • Digital • Merchandising

10,000+

Description

• Serve as an integral member of a multi-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 ahead of 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.

Requirements

• Bachelor’s degree or a combination of relevant education, training, and experience in the field of ML Engineering or Software Engineering • One year of experience preferred • 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 • Strong experiential 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, Dask, TensorFlow, Kubeflow, Spark, EMR, 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.