Manager, Data & ML Platform Engineer at LA Clippers in Inglewood, CA

Summary: The LA Clippers are seeking a Manager, Data & ML Platform Engineer to build and operate their data and machine learning infrastructure. This role involves overseeing the development of data pipelines, feature engineering, model deployment, and ensuring the reliability of AI systems that enhance fan experiences and operations. Responsibilities: - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 Required Qualifications: - 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 Preferred Qualifications: - 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 Required Skills: Data engineering, Machine learning engineering, MLOps, Data pipelines, Data modeling, Structured, unstructured data systems, Machine learning model deployment, API development, Real-time inference systems, CI/CD pipelines for ML, Containerization Docker, Containerization Kubernetes, Feature engineering, Feature stores, Data pipeline monitoring, Model monitoring, Automated retraining workflows, Python, SQL, Cloud platforms AWS, Cloud platforms GCP, Cloud platforms Azure, Distributed systems, Ownership mindset Benefits: Medical, Dental and Vision plans, 401(k) plan with company contribution, Wellbeing Allowance of up to $1,000 per year, Paid vacation and sick time, Paid parental leave, Flexible Spending Accounts (Medical and Dependent Care), Company-paid Long Term Disability insurance, Company-paid Life and AD&D Insurance, Voluntary Life Insurance options for employee, spouse and children, Employee Assistance Program, Mind health support via Modern Health and Headspace