Manager, Data & ML Platform Engineer

LA Clippers•Inglewood, CA

1d•$200,000 - $230,000•Onsite

About The Position

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.

Requirements

5–10+ years of experience in data engineering, machine learning engineering, or MLOps
Proven track record building end-to-end data and ML systems in production environments
Experience supporting multiple data and ML use cases at scale (e.g., forecasting, personalization, recommendation systems, or operational analytics)
Strong experience building and maintaining data pipelines (batch and streaming) using modern data stack tools
Experience designing data models and schemas for analytics and machine learning use cases
Experience working with large-scale structured and unstructured data systems
Experience deploying machine learning models into production, including API-based and real-time inference systems
Experience building CI/CD pipelines for ML workflows, including testing, versioning, and deployment
Experience with containerized environments (Docker, Kubernetes or similar)
Experience building feature pipelines or feature stores for machine learning
Experience enabling low-latency data access for real-time decisioning systems
Experience implementing data pipeline monitoring and model monitoring (performance, drift, reliability)
Experience designing automated retraining and lifecycle management workflows
Strong proficiency in Python and SQL
Experience building production-grade services and APIs
Experience working with cloud platforms (AWS, GCP, or Azure) and distributed systems
Experience partnering with data scientists to productionize models and analytical workflows
Ability to translate business needs into scalable data and ML systems
Strong ownership mindset with the ability to design, build, and operate systems end-to-end

Nice To Haves

Experience building internal data + ML platforms used by multiple teams
Experience with LLM infrastructure (embeddings, vector search, RAG pipelines)
Experience with real-time decisioning systems (pricing, recommendations, personalization)
Experience building experimentation platforms or A/B testing infrastructure
Experience with deep learning workflows or GPU-based training systems
Experience in consumer tech, sports, marketplaces, or entertainment industries

Responsibilities

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.
Feature Engineering & Real-Time Data Systems Design and implement feature pipelines and feature stores that power machine learning models across pricing, forecasting, and personalization. Enable low-latency data access for real-time inference and decision systems. Standardize reusable feature definitions to ensure consistency across models and teams.
Model Deployment & ML Systems Build and operate systems for deploying machine learning models into production, including APIs and real-time inference services. Implement CI/CD pipelines for ML models, enabling automated testing, versioning, and deployment. Enable real-time decisioning systems for pricing, recommendations, and fan engagement.
Model Monitoring & Lifecycle Management Implement monitoring systems that track model performance, drift, data quality, and system reliability. Build automated workflows for model retraining, versioning, and lifecycle management. Establish standards for observability, logging, and model governance.
AI Product & Experimentation Infrastructure Build infrastructure that supports A/B testing, experimentation, and model evaluation across AI-driven products. Enable rapid iteration and measurement of pricing strategies, personalization models, and marketing optimization systems.
LLM & AI Application Enablement Support infrastructure for AI-powered customer interaction systems, including chatbots, copilots, and fan-facing AI agents. Build and maintain systems for embeddings, vector search, and retrieval pipelines where applicable.
Cross-Functional Delivery & Technical Leadership Partner with data scientists, product, and business teams to translate AI models into production systems that drive measurable impact. Act as a hands-on technical leader, helping define architecture, standards, and best practices for data and ML systems. Identify opportunities to improve performance, scalability, and speed of delivery across the data and AI platform.