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, ensuring effective model deployment and monitoring.
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
Company H1B Sponsorship