Staff Machine Learning Engineer: Personalization

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/DynamoDB 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 (RecSys) 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/PubSub) and low-latency lookups (Redis, DynamoDB) to serve model inference in <200ms.
• MLOps Experience: Experience with the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, or Databricks.
• Strong Coding Skills: Expert in Python and SQL; proficiency in Go or Rust is a strong plus for high-performance inference layers.
• Cloud Native: Deep experience with GCP services (BigQuery, Cloud Functions, GKE) or AWS equivalents.

• Experience implementing "bandit" algorithms or reinforcement learning for content ranking.
• Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading.
• Experience building "Feature Stores" that bridge batch historical data with real-time event streams.

• While we prefer candidates based in Atlanta, we are open to qualified applicants from anywhere in the U.S. and are willing to consider remote candidates. #LI-Remote 

The typical salary range for this position is $220,000 to $280,000. At PrizePicks, we consider your role, level, and where you'll be working when determining our salary ranges. The compensation info you see on our job postings gives you an idea of the starting pay range for the position. Your actual pay within that range will depend on your specific work location, as well as your skills, experience, and education. Your recruiter will be happy to chat more about the specific pay range for your location and how we arrived at it during the hiring process. 

This application period will remain open for 30 days. We’re committed to finding the best candidate, so this date may be adjusted, and any changes will be reflected in this posting.

Date Posted: 2/4/2026