Lead Machine Learning Engineer - AI/ML

October 9

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Nike

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Athletic Shoes • Apparel • Sports Equipment • Digital • Merchandising

10,000+

Description

• Nike is looking for a Lead Machine Learning Engineer to join our growing team. • This role is part of a squad within Enterprise Data & AI AI/ML team, that is responsible for building solutions that will transform marketing experiences and workflows at Nike. • Playing a key role in the development of digital tools, processes, and experiences, the ideal candidate is a problem-solver who is motivated to learn new technologies, communicate ideas and knowledge, and collaborate with teammates. • You will work with a cross-functional team to build solutions. • You need to be able to work with ambiguity and abstract requirements, developing features quickly in a team context, balancing speed with quality. • Your work will accelerate Nike's core mission of serving Athletes.

Requirements

• Bachelor Degree or a combination of relevant education, training and experience. • Advanced degrees a plus (PhD, Masters, etc.) • 5+ plus years of experience in enterprise environment with a combination of technology and team leadership responsibilities. • Expertise with Python, Spark, or Java. • Expertise in NodeJS, React, or Vue.js a plus • Strong leadership skills to mentor and guide a team of Machine Learning Engineers. • Balance of technical expertise, strategic planning, and team management to ensure project execution and workforce optimization. • Expertise in building and productionalizing large scale consumer facing ML models. • Designed, built and shipped applications that scale and implementing best practices in ML Ops and CI/CD to build state of the art ML models. • Proficient at writing good quality, well-documented and tested, scalable code - Python preferred. • Experience with tools like mlFlow, Airflow, Docker and Cloud Platforms such as AWS/GCP is ideal. • Experience deploying, monitoring and maintaining data science products in cloud environments such as AWS. • Knowledge of techniques for model compression, quantization, and optimization for deployment in resource-constrained environments. • Experience with data processing and storage frameworks like S3, Spark, Dynamo, etc.