Staff Machine Learning Engineer
PrizePicks is seeking a Staff Machine Learning Engineer to lead the technical efforts in scaling and productionising core machine learning capabilities. This role involves designing and building end-to-end machine learning infrastructure to transition experimental models into high-availability production services.
The successful candidate will influence key platform metrics such as Time-to-Bet, Deposit Velocity, and Platform Integrity. By integrating low-latency machine learning models across the sports betting and daily fantasy ecosystems, the engineer will play a critical role in PrizePicks' technical strategy.
- Design and build the end-to-end machine learning infrastructure, transitioning experimental Data Science models into robust, high-availability production services.
- Steer the design and deployment of low-latency services to serve model inferences in milliseconds.
- Partner with Data Science to build scalable logging and data pipelines, leading the creation and optimisation of a centralised feature store.
- Champion best practices for model deployment, monitoring, and CI/CD for machine learning.
- Implement automated retraining pipelines and observability tools to ensure data drift and model degradation are addressed instantly.
- 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 (required)
- 3+ years of technical leadership, acting as a lead and driving architecture decisions for consumer applications or scalable backend platforms (required)
- Proficiency in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in <100ms (required)
- Deep experience managing the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, Databricks, or SageMaker (required)
- Expert in Python and SQL (required)
- Proficiency in Go, C++, or Rust (nice-to-have)
- Deep experience with GCP services (BigQuery, Cloud Functions, GKE, Vertex AI) or AWS equivalents (required)
- Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing, risk management, or fraud detection (nice-to-have)
- Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading (nice-to-have)
- Experience building and scaling feature stores that bridge batch historical data with real-time event streams (nice-to-have)
- The typical salary range for this position is $220,000 to $280,000.
- Company-subsidised medical, dental, & vision plans
- 401(k) plan with company match
- Annual bonus
- Flexible PTO to encourage a healthy work/life balance
- 16-week paid parental leave and disability benefits
- Workplace flexibility and modern work schedules
- Company-wide in-person events and team outings
- Lifestyle enhancement program
- Company equipment provided
PrizePicks is a daily fantasy sports operator headquartered in Atlanta, Georgia. Its app lets players predict whether athletes will beat or fall short of projected statistics, and it has added prediction-market contracts regulated by the US Commodity Futures Trading Commission. In January 2026 the lottery and gaming group Allwyn completed the purchase of a 62.3% majority stake, with PrizePicks continuing as a standalone brand.