Machine Learning Platform Engineer

Role Overview

As a Machine Learning Platform Engineer at PrizePicks, you will build and scale the ML platform that productionizes core machine learning capabilities. Your work will help improve key business metrics (e.g., Time-to-Bet, Deposit Velocity, and Platform Integrity) by deploying robust, low-latency ML models across the sports betting and daily fantasy ecosystem.

What You’ll Do

• Build scalable ML systems: Design and implement end-to-end ML infrastructure and enable experimental Data Science models to transition into reliable, high-availability production services.
• Real-time inference at scale: Automate deployment of low-latency inference services capable of serving predictions in milliseconds.
• Feature engineering & data strategy: Lead creation and optimization of a centralized feature store to support training complex models across multiple business domains.
• End-to-end MLOps: Partner with Infrastructure to build and operate ML platform components for training and experimentation with a focus on developer experience.
• Deployment best practices: Establish practices for model deployment, monitoring, and ML CI/CD.
• Automation & observability: Implement automated retraining pipelines, plus observability to detect data drift and model degradation quickly.

What You Have

• 3+ years of Platform Engineering experience, including 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/streaming architectures and low-latency inference services (Kafka/Flink/PubSub).
• Strong MLOps expertise across the full ML lifecycle (training, deploying, monitoring), including tools such as Amazon SageMaker and Vertex AI, plus vector databases and graph databases.
• Experience managing and scaling caches such as Redis or Elasticsearch.
• Strong containerization/orchestration skills: Docker and Kubernetes.
• Expertise in Python; Go, C++, or Rust is a plus for high-performance inference.

What Makes You Stand Out

• Experience enforcing infrastructure and deployment best practices for ML platforms.
• 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.
• Ability to enable self-service for ML/Data Science teams.
• Experience enabling AI agents and AI coding to accelerate development.

Location / Remote Policy

• Prefer Atlanta, GA, but open to qualified candidates from anywhere in the U.S. and considering remote candidates.

Compensation

• $155,000 to $185,000 typical salary range (adjusted based on role level and location).