Machine Learning Platform Engineer

About the Role

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: Lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains.
• End-to-End MLOps: Work with the Infrastructure team to build and operate core ML platform components for training and experimentation, with a focus on developer experience. Champion best practices for model deployment, monitoring, and CI/CD for ML. Implement automated retraining pipelines and observability for ML systems to ensure data drift and model degradation are caught and addressed instantly.

Qualifications

• 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 and deep experience building a platform for managing the full ML lifecycle (training, deploying, monitoring) using tools like SageMaker, VertexAI, Vector DBs, Graph Databases, and managing/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 platforms.
• 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.