Machine Learning Engineer: Personalization

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At Prize Picks, we are the fastest-growing sports company in North America, as recognized by Inc. 5000. As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 450 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports fandom.

Ready to reimagine the DFS industry together?

As a Staff Machine Learning Engineer, Personalization you will lead the technical charge to move us from static feeds to a "Cohort-First, Individual-Next" personalization strategy. Your work will directly impact Time-to-Bet and Deposit Velocity by ensuring no user has to scroll endlessly for relevant sports and markets based on their preferences.

• Architect the Hybrid Engine: Design and build the "Project Bridge" architecture, transitioning the platform from heuristic-based logic (Cohort/Geo-based) to fully real-time ML personalization (Vector Search/Neural Networks).
• Real-Time Inference at Scale: Steer the design and deployment of low-latency services (Segment Service & User Profile Service) using Redis/Dynamo
  
  DB to serve personalized board orderings, deposit defaults, and "For You" feeds in milliseconds.
• Feature Engineering & Data Strategy: Partner with Data Science to build the logging pipelines that tag why a user saw an item (data labeling). You will create the feature store required to train future neural networks for individual-level personalization.
• Solve the "Cold Start" Problem: Implement logic for dynamic league ordering and deposit smart-defaults based on geospatial data and initial user cohorts, ensuring immediate relevance for new users.

• 7+ years of experience in Backend/ML Engineering with a specific focus on Recommendation Systems (Rec Sys) or Personalization engines in production.
• 3+ years of technical leadership
  , acting as a lead and driving architecture decisions for high-traffic consumer applications.
• Experience with Real-Time Data: Proficient in streaming architectures (Kafka/Pub Sub) and low-latency lookups (Redis, Dynamo
  
  DB) to serve model inference in