Staff Machine Learning Engineer

PrizePicks is a sports technology company and Daily Fantasy Sports platform serving sports leagues and esports titles. The Staff Machine Learning Engineer will lead the development and productionization of scalable machine learning systems, including low-latency inference services, feature stores, data pipelines, and MLOps capabilities. The role will support real-time decisions across sports betting and daily fantasy applications while driving technical architecture and leadership.

Responsibilities:

• Architect Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, transitioning experimental Data Science models into robust, high-availability production services
• Real-Time Inference at Scale: Steer the design and deployment of 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: Partner with Data Science to build scalable logging and data pipelines. 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 Leadership: Champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability tools to ensure data drift and model degradation are caught and addressed instantly

Requirements:

• 7+ years of experience in Machine Learning Engineering or Backend Engineering, with a proven track record of deploying and maintaining complex ML models in high-traffic production environments
• 3+ years of technical leadership, acting as a lead and driving architecture decisions for consumer applications or scalable backend platforms
• Proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in <100ms
• Deep experience managing the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, Databricks, or SageMaker
• Expert in Python and SQL; proficiency in Go, C++, or Rust is a strong plus for building high-performance inference layers
• Deep experience with GCP services (BigQuery, Cloud Functions, GKE, Vertex AI) 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
• Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing, risk management, or fraud detection
• 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
• 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