Note: The job is a remote job and is open to candidates in USA. Fanatics is building a leading global digital sports platform. They are seeking a Machine Learning Engineer III to own the infrastructure and systems that bring data science models to life at scale, enabling teams to unlock greater value for customers through data and analytics.
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
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 production ML workflows, including packaging, versioning, deployment, and monitoring
- Hands-on experience with end-to-end ML platforms such as Databricks, AWS SageMaker, or equivalent, including model registry and serving components
- Proven experience building real-time feature pipelines and model serving systems that operate at scale with strict latency and uptime requirements
- Experience building or scaling recommendation or ranking systems in production, including embedding pipelines and low-latency inference infrastructure
- Solid understanding of distributed systems and large-scale data processing (e.g. Spark, Kafka, or equivalent)
- Strong SQL proficiency and experience working with relational and dimensional data models
- Practical understanding of the mathematics underlying modern ML (linear algebra, probability, optimization) sufficient to partner effectively with Data Scientists on model design and debugging
- Familiarity with experimentation infrastructure and A/B testing frameworks, including exposure bias handling and metric integrity in production environments
- Experience with feature stores (e.g. Feast, Tecton) and their role in supporting both real-time and batch ML use cases
- Experience with ML observability tooling, including drift detection, prediction monitoring, feature freshness alerting
Benefits
- In addition to the base and bonus, full-time employment, and more. For information about our benefits, please visit
Company Overview
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