Machine Learning Platform Engineer

Role Overview

As a Machine Learning Platform Engineer at PrizePicks, you will build and productionize the company’s machine learning platform. Your work will scale low-latency ML models and directly impact key business metrics such as Time-to-Bet, Deposit Velocity, and Platform Integrity.

Responsibilities

• Build scalable ML systems: Design and implement end-to-end ML infrastructure, enabling experimental Data Science models to transition into high-availability production services.
• Real-time inference at scale: Create automation to deploy low-latency inference services that return predictions in milliseconds.
• Feature engineering & data strategy: Lead the creation and optimization of a centralized feature store to support training across multiple business domains.
• End-to-end MLOps:
  • Partner with Infrastructure to build and operate core ML platform components for training and experimentation (with strong developer experience).
  • Implement best practices for model deployment, monitoring, and ML CI/CD.
  • Build automated retraining pipelines and observability to detect data drift and model degradation.

Requirements

• 3+ years in platform engineering, with experience deploying and maintaining scalable ML platforms in high-traffic production.
• 1+ years owning ML systems end-to-end in production, including on-call and incident response.
• Experience with real-time data and streaming architectures (Kafka/Flink/PubSub) and building low-latency inference services.
• Strong MLOps expertise across the ML lifecycle (training, deploying, monitoring) using tools such as SageMaker and/or Vertex AI.
• Experience with vector databases and graph databases.
• Experience managing and scaling caches such as Redis or Elasticsearch.
• Containerization and orchestration: Docker, Kubernetes, and cluster-level management.
• Python expertise; proficiency in Go is required. (C++ or Rust is a plus.)

Nice-to-Haves / Stand-Out Skills

• Experience enforcing infrastructure best practices for ML platform deployments.
• Background in Daily Fantasy Sports (DFS), odds-making, or high-frequency trading.
• Experience building and scaling Feature Stores bridging batch historical data with real-time event streams.
• Experience enabling self-service for ML/Data Science teams for model development and deployment.
• Experience enabling AI agents and AI coding for faster development cycles.

Location / Remote Policy

Prefer candidates based in Atlanta, GA, but the company is open to qualified applicants from anywhere in the U.S. and may consider remote candidates.

Salary

Typical salary range: $135,000–$160,000 (based on role level and location).