Machine Learning Platform Engineer

PrizePicks is a rapidly growing sports company and leading platform for Daily Fantasy Sports across major sports leagues and esports. The Machine Learning Platform Engineer will build and scale the ML platform, productionize machine learning capabilities, and enable low-latency model inference across sports betting and daily fantasy ecosystems. The role also focuses on feature engineering, MLOps, model deployment, monitoring, automated retraining, and platform developer experience.

Responsibilities:

• Design and build the end-to-end machine learning infrastructure, setup platform for transitioning experimental Data Science models into robust, high-availability production services
• 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
• You will lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains
• 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

Requirements:

• 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
• 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
• 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
• 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