Machine Learning Engineer III - FES

About Us

Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet.

About The Team

We are the Fan Ecosystem Data team, responsible for enhancing decision-making and innovation across the entire Fanatics ecosystem through data and analytics. We build products that turn disparate data streams into real-time actionable insights, empowering teams to unlock greater value for our customers and stakeholders across every Fanatics surface.

We are seeking a Machine Learning Engineer III to own the infrastructure and systems that bring our data science models to life at scale. As our Data Scientists and Data Engineers build the models that understand and predict fan behavior, you build the platforms that serve those models in production.

Responsibilities

• Own the end-to-end ML infrastructure for recommendation, personalization, and LTV scoring systems, from feature engineering through model deployment and monitoring.
• Build and maintain real-time and batch feature pipelines that serve low-latency predictions across the FanApp recommendation experience and cross-vertical personalization use cases.
• Develop and scale model serving infrastructure that supports high-throughput, high-availability prediction across Fanatics' multi-product ecosystem.
• Partner directly with Data Scientists to productionize LTV, churn, propensity, and ranking models and bridge the gap between experimentation and reliable production systems.
• Build and maintain embedding pipelines that generate and refresh user and item representations powering personalization and affinity modeling at scale.
• Implement and maintain A/B testing and experimentation infrastructure that enables reliable measurement of model and feature impact in production.
• Collaborate with Data Engineers, Analytics Engineers, and Product teams to identify data sources, enforce data quality standards, and ensure models are fed with accurate, timely signals.
• Drive continuous improvement of model accuracy, latency, and throughput through iterative optimization and monitoring frameworks.

Experience And Skills

• 3–5+ years in a machine learning engineering or data engineering role, with a degree in a quantitative field (Computer Science, Mathematics, Statistics, Engineering, or equivalent).
• Strong Python proficiency and deep familiarity with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and production ML systems (e.g., feature stores, model registries, serving infrastructure).
• Experience building and maintaining real-time and batch data pipelines using tools like Spark, Kafka, Flink, or similar.
• Proven ability to deploy, monitor, and debug machine learning models in production environments.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
• Strong understanding of software engineering best practices, CI/CD, and version control.
• Excellent communication and collaboration skills, with a track record of partnering effectively with data scientists and cross-functional teams.