Senior Machine Learning Platform Engineer

PrizePicks is the fastest-growing sports company in North America, recognized for its leading platform in Daily Fantasy Sports. As a Senior ML Platform Engineer, you will contribute to building the ML platform to scale and productionize core machine learning capabilities, impacting key metrics across the sports betting and daily fantasy ecosystems.

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

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 - 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
• 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