Machine Learning Platform Engineer

Job summary

PrizePicks is seeking a Machine Learning Platform Engineer to build and scale their core machine learning capabilities, directly impacting key metrics like Time-to-Bet and Deposit Velocity. The role involves designing end-to-end ML infrastructure, implementing real-time inference at scale, and leading feature engineering and data strategy. This position requires expertise in MLOps, containerization, and programming languages like Python and Go.

Benefits

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

Responsibilities

• Build scalable ML systems
• Design and build end-to-end machine learning infrastructure
• Set up platform for transitioning experimental Data Science models into robust, high-availability production services
• Build automation for deploying low-latency services to serve model inferences in milliseconds
• Power real-time decisions across the platform, from dynamic oddsmaking and risk analysis to smart deposit defaults
• Lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains
• Work with the Infrastructure team to build and operate core ML platform components for training and experimentation enablement considering 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

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