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Strong employer brand, metro location, and a visible ML engineering title increase applicant competition.
Core ML engineering, MLOps, and cloud skills transfer easily across industries, so background fit sensitivity is low.
Explicit 8+ years, 3+ years ML, and mandatory MLOps/tech stack requirements make filtering strict.
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Own end-to-end delivery of machine learning and generative AI projects impacting Nike’s corporate functions.
Set technical direction, provide leadership and guidance, and raise engineering quality within the team.
Design and implement scalable AI applications leveraging prediction and optimization models to drive business decisions.
Undergraduate degree in Computer Science, Master's in related field, or equivalent experience.
8+ years professional software engineering experience with at least 3 years in Machine Learning Engineering or related fields.
Proficiency in Python, AWS (ECR, SageMaker, Lambda, API Gateway), databases (Postgres, Redis), and data processing technologies (SageMaker or Databricks).
Experience leading teams, mentoring engineers, and working in an agile product environment.
Experienced technical leader comfortable influencing technical strategy and mentoring in agile, product-oriented teams.
Strong expertise in MLOps, predictive/mathematical optimization model production, and cloud-native AI/ML architectures.
Ability to work collaboratively in a globally distributed team to solve machine learning problems at scale.