As a 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.

What you’ll do:

• 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 to deploy low-latency services that 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, with a focus on 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.

What you have:

• 3+ years of experience in Platform Engineering, with a proven track record of deploying and maintaining a scalable ML platform in high-traffic production environments. 
• 1+ 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.

What makes you stand out:

• 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.

Where you’ll live:

• 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 

Working at PrizePicks:

The typical salary range for this position is $155,000 to $185,000. 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:  8/3/2026