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This Los Angeles-based role is a hands-on opportunity for a Manager, Data & Machine Learning Platform Engineer to design and build end-to-end data and ML systems that power intelligent products across fan engagement, marketing, and operations. Acting as an individual contributor, you will own the full lifecycle of data and machine learning platforms—from data ingestion to real-time model deployment—enabling scalable, production-ready AI solutions.
Job Responsibility
Design, build, and maintain scalable data pipelines and ML platform infrastructure for analytics and AI use cases
Own the end-to-end lifecycle of data and ML systems, including ingestion, transformation, feature engineering, deployment, and monitoring
Develop and optimize data models and schemas to support both batch and real-time workflows
Build and manage feature pipelines and enable low-latency access for real-time decisioning systems
Deploy machine learning models into production via APIs and real-time inference services
Implement CI/CD pipelines for machine learning workflows, including testing, versioning, and automated deployment
Establish monitoring systems to track data quality, model performance, and system reliability
Enable experimentation frameworks such as A/B testing to support data-driven product iteration
Collaborate cross-functionally with data scientists, product teams, and business stakeholders to deliver impactful AI solutions
Drive architectural decisions and promote best practices in data engineering and MLOps
Requirements
4+ years of experience in data engineering, machine learning engineering, or MLOps
Proven track record of building end-to-end data and ML systems in production environments
Strong proficiency in Python and SQL
Experience with cloud platforms (Azure, AWS, or GCP) and distributed systems
Expertise in building and maintaining data pipelines (batch and streaming)
Experience deploying ML models into production, including real-time inference systems
Hands-on experience with CI/CD pipelines for ML workflows and containerization tools (e.g., Docker, Kubernetes)
Strong background in designing scalable data models and working with large-scale datasets
Nice to have
Experience with modern data platforms such as Databricks, Snowflake, or similar tools
Exposure to feature stores and real-time data processing systems
Experience with LLM infrastructure, embeddings, or vector search
Background in building experimentation or A/B testing platforms
Experience working in consumer-facing industries such as sports, entertainment, or digital platforms
Familiarity with personalization, recommendation systems, or forecasting models
What we offer
Comprehensive benefits package including medical, dental, and vision coverage
401(k) with company contribution
Annual wellbeing allowance
Flexible paid time off and parental leave
Company-paid life and disability insurance
Mental health and wellness support programs
Flexible spending accounts and family planning assistance