Manager, Data & ML Platform Engineer

About the Job

The LA Clippers have built the most seamless fan experience in live sports at the Intuit Dome. Data and AI are central to creating a true home-court advantage — powering everything from ticket demand forecasting to personalized fan experiences and AI-driven customer service.

The Lead Data & ML Platform Engineer is responsible for building and operating both the data platform and machine learning infrastructure that power AI products across the LA Clippers and Intuit Dome ecosystem. This role combines full stack data engineering and MLOps, owning the pipelines, data models, and platform systems that enable machine learning models to be developed, deployed, and operated reliably in production. 

Working closely with analysts, data scientists, product, and business teams, this role ensures that fan, ticketing, marketing, and operational data is transformed into high-quality, real-time datasets and production-ready AI systems. The platform supports key AI products including dynamic ticket pricing, demand forecasting, fan personalization, customer interaction intelligence, and marketing optimization.

The ideal candidate has experience building end-to-end data and ML systems, from ingestion and transformation through model productionization and real-time decisioning.

This is a full-time role based in Inglewood, CA in our new home at Intuit Dome. You will be eligible for our competitive benefit offering including medical, dental, vision, 401(k) plan with company contribution, Well-Being Allowance, and more.

 

What You Will Do: 

End-to-End Data & ML Platform Development

• Design, build, and operate the data and machine learning platform that powers AI products across ticketing, marketing, fan experience, and operations.
• Own the full lifecycle from data ingestion and transformation → feature engineering → model deployment → monitoring and continuous improvement.

Data Engineering & Data Platform

• Build and maintain scalable data pipelines and transformations that unify CRM, ticketing, behavioral, and operational data into high-quality, analytics- and AI-ready datasets.
• Develop and optimize data models and schemas that support real-time and batch use cases across analytics and machine learning.
• Ensure data reliability, quality, and performance, including pipeline monitoring, testing, and optimization.&l