PrizePicksRemote
Staff Machine Learning Engineer: Personalization
United States of America
Full Time
2 hours ago
$220,000 - $280,000 USD
No H1B
Key skills
Machine Learning EngineeringRecommendation SystemsReal-Time Data ProcessingCloud ServicesFeature EngineeringPythonSQLGoRustMLOpsData StrategyCold Start Problem SolvingBandit AlgorithmsReinforcement LearningFeature StoresMachine LearningMLMLflowKubeflowBigQueryDatabricksKafkaAWSGCPGKEDynamoDBCloud FunctionsRedisLeadership
About this role
PrizePicks is the fastest-growing sports company in North America, recognized for its leading platform in Daily Fantasy Sports. As a Staff Machine Learning Engineer focusing on Personalization, you will architect and implement a real-time ML personalization strategy to enhance user experience and engagement.
Responsibilities:
- 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
Requirements:
- 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 MLOps
- 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