Staff Machine Learning Engineer

PrizePicks is the fastest-growing sports company in North America, recognized for its leading platform for Daily Fantasy Sports. As a Staff Machine Learning Engineer, you will lead the development of scalable machine learning systems that enhance key metrics across sports betting and fantasy ecosystems.

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
• Experience with Real-Time Data: Proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference
• MLOps Expertise: Deep experience managing the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, Databricks, or SageMaker
• Strong Coding Skills: Expert in Python and SQL; proficiency in Go, C++, or Rust is a strong plus for building high-performance inference layers
• Cloud Native: Deep experience with GCP services (BigQuery, Cloud Functions, GKE, Vertex AI) or AWS equivalents
• 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