Staff Machine Learning Engineer
About The Position
At PrizePicks, we are the fastest-growing sports company in North America, as recognized by Inc. 5000. As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 550 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports fandom. Ready to reimagine the DFS industry together? As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize our core machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet, Deposit Velocity, and Platform Integrity by integrating robust, low-latency ML models across our sports betting and daily fantasy ecosystems.
Requirements
- 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.
- 3+ years of technical leadership, acting as a lead and driving architecture decisions for consumer applications or scalable backend platforms.
- 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 managing the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, Databricks, or SageMaker.
- Strong Coding Skills: Expert in Python and SQL; proficiency in Go, C++, or Rust is a strong plus for building high-performance inference layers.
- Cloud Native: Deep experience with GCP services (BigQuery, Cloud Functions, GKE, Vertex AI) or AWS equivalents.
Nice To Haves
- Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing, risk management, or fraud detection.
- 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.
Responsibilities
- Architect Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, transitioning experimental Data Science models into robust, high-availability production services.
- Real-Time Inference at Scale: Steer the design and deployment of low-latency services to serve model inferences in milliseconds. You will power real-time decisions across the platform, from dynamic oddsmaking and risk analysis to smart deposit defaults.
- Feature Engineering & Data Strategy: Partner with Data Science to build scalable logging and data pipelines. You will lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains.
- End-to-End MLOps Leadership: Champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability tools to ensure data drift and model degradation are caught and addressed instantly.
Benefits
- Company-subsidized medical, dental, & vision plans
- 401(k) plan with company match
- Annual bonus
- Flexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!)
- Generous paid leave programs, including 16-week paid parental leave and disability benefits
- Workplace flexibility and modern work schedules focused on getting the job done, not hours clocked
- Company-wide in-person events and team outings
- Lifestyle enhancement program
- Company equipment provided (Windows & Mac options)
- Annual performance reviews with opportunities for growth and career development
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
