Senior Machine Learning Platform Engineer

PrizePicks•Atlanta, GA

12h•Remote

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 550 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 Senior ML Platform Engineer, you will contribute to building the ML platform at Prizepicks to scale and productionize our core machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet, Deposit Velocity, and Platform Integrity by integrating robust, low-latency ML models across our sports betting and daily fantasy ecosystems.

Requirements

5+ years of experience in Platform Engineering, with a proven track record of deploying and maintaining a scalable ML platform in high-traffic production environments.
2+ years of experience owning ML systems end-to-end in production, including on-call and incident response.
Experience with Real-Time Data, proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in <100ms.
MLOps Expertise, deep experience building a platform for managing the full ML lifecycle (training, deploying, monitoring) using tools like SageMaker, VertexAI, Vector DBs, Graph Databases. Managing and scaling caches like Redis or Elasticsearch.
Proficient with Containerization, Docker, Kubernetes, and cluster-level management.
Expert in Python, proficiency in Go. C++, or Rust is a strong plus for building high-performance inference layers.

Nice To Haves

Experience implementing infrastructure while enforcing best practices for the deployment of ML Platform.
Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading.
Experience building and scaling "Feature Stores" that successfully bridge batch historical data with real-time event streams.
Enabling self-service for ML and Data Science teams for model development and deployment.
Enabling AI agents and AI coding for faster and iterative software development.

Responsibilities

Build Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, setup platform for transitioning experimental Data Science models into robust, high-availability production services.
Real-Time Inference at Scale: Build automation for deploying low-latency services to serve model inferences in milliseconds. You will power real-time decisions across the platform, from dynamic oddsmaking and risk analysis to smart deposit defaults.
Feature Engineering & Data Strategy: You will lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains.
End-to-End MLOps: You will work with the Infrastructure team to build and operate core ML platform components for training and experimentation enablement considering developer experience. You will champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability for ML systems to ensure data drift and model degradation are caught and addressed instantly.

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