Staff Machine Learning Engineer: Personalization
About The Position
At PrizePicks, 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.
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 <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.
- You must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
Nice To Haves
- 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.
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.
Benefits
- Company-subsidized medical, dental, & vision plans
- 401(k) plan with company match
- Annual bonus
- Flexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!)
- Generous paid leave programs, including 16-week paid parental leave and disability benefits
- Workplace flexibility and modern work schedules focused on getting the job done, not hours clocked
- Company-wide in-person events and team outings
- Lifestyle enhancement program
- Company equipment provided (Windows & Mac options)
- Annual performance reviews with opportunities for growth and career development
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
