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# Staff Machine Learning Engineer

- **Company:** Prizepicks Llc
- **Location:** Atlanta, GA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $220,000.0 - $280,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, BigQuery, C++ (Programming Language), Cloud Engineering, Continuous Integration, Experimental Data, Fraud Prevention and Detection, High-Frequency Trading, Python (Programming Language), Machine Learning, SQL Databases, Data Streaming, Reinforcement Learning, Data Logging, Google Cloud, Feature Engineering, Backend, Data Strategy, Build Management, Kubernetes, Apache Flink, Real Time Data, Apache Kafka, Machine Learning Operations, Software Coding, Data Pipelines, Databricks
- **Published:** July 17, 2026
- **Apply:** https://www.dice.com/job-detail/40360faa-19c8-43ef-b53e-17bc33d03c46

## About the Role

* 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 Google Cloud Platform services (BigQuery, Cloud Functions, GKE, Vertex AI) or AWS equivalents.

What makes you stand out:

 * 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., You must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

## Description

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.
What you'll do:

 * 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.


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