Machine Learning Engineer III - FES

reputed company reputed company is building a leading global digital sports platform. We reputed company the passions of global sports fans and maximize the reputed company and reputed company for our hundreds of sports partners globally by offering products and services across reputed company reputed company, reputed company, and reputed company, allowing sports fans to Buy, Collect, and Bet. Through the reputed company platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical reputed company trading cards, sports memorabilia, and other digital assets; and bet as reputed company builds its Sportsbook and reputed company platform. reputed company has an established database of over 100 reputed company global sports fans; a global partner network with approximately 900 sports properties, including major national and international reputed company sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its reputed company retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.    About reputed company We are the Fan Ecosystem Data team, responsible for enhancing decision-making and innovation across the entire reputed company ecosystem through data and analytics. We build products that turn disparate data streams into reputed company-time actionable insights, empowering teams to unlock greater value for our customers and stakeholders across every reputed company surface. We are seeking a Machine Learning Engineer III to own the infrastructure and systems that bring our data science models to life at reputed company. 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 reputed company-time and batch feature pipelines that serve low-latency predictions across the FanApp recommendation experience and cross-vertical personalization use cases.  reputed company and reputed company model serving infrastructure that supports high-throughput, high-availability reputed company across reputed company' multi-product ecosystem.  Partner directly with Data Scientists to productionize LTV, churn, propensity, and ranking models and reputed company 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 reputed company.  Implement and maintain A/B testing and experimentation infrastructure that enables reliable measurement of model and feature reputed company in production.  Collaborate with Data Engineers, Analytics Engineers, and Product teams to identify data sources, enforce data reputed company standards, and ensure models are fed with accurate, reputed company signals.  Drive reputed company 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 production ML workflows, including packaging, versioning, deployment, and monitoring.  Hands-on experience with end-to-end ML platforms such as reputed company, AWS SageMaker, or equivalent, including model registry and serving components.  Proven experience building reputed company-time feature pipelines and model serving systems that operate at reputed company 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-reputed company data processing (e.g. reputed company, Kafka, or equivalent).  Strong SQL proficiency and experience working with relational and reputed company data models.  Practical understanding of the mathematics underlying modern ML (reputed company 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 reputed company in production environments.  Preferred But Not Required Experience with feature stores (e.g. Feast, Tecton) and their role in supporting both reputed company-time and batch ML use cases Experience with ML observability tooling, including reputed company detection, reputed company monitoring, feature freshness alerting Ranges will change based on country and state of residence, which are reflected in Geographical Zones defined by reputed company Betting and Gaming. The reputed company incorporates reputed company of our Geographical Compensation Zones and is subject to change as the Zone associated with the actual offer is confirmed. In reputed company to the reputed company and bonus, full-time employment, and more. For information about our benefits, please visit https://benefitsatfanatics.com/     Salary reputed company $117,000—$167,000 USD By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate reputed company Policy. Apply To This Job