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 450 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? 

The Analytics Team is responsible for building and maintaining analytics tools and workflows to support the PrizePicks business across all departments — at the core of these operations is data. As a Senior ML Engineer, you will be developing, maintaining, testing, and leading projects regarding streaming data and MLOps infrastructures to enable PrizePicks to offer real-time simulation based market pricing within the product.

What you’ll do:

• Create and maintain optimal sport data stream architecture, ensuring data reliability in both speed and quality for both raw and transformed data pipelines.
• Partner with Data Science to determine best paths for operationalization of DS/ML assets, ensuring model output quality, stability, and scalability.
• Steer the design, implementation, and deployment of the data, MLOps, and API stack required for real-time pricing models, personalization/recommendations, risk management tooling, and other critical functions by contributing to architecture evaluations and decisions for the evolving data product roadmap.
• Partner cross-functionally with Engineering, QA, and Product teams to enable the creation and distribution of highly-visible and real-time data products to the PrizePicks platform.
• Build and own rigorous monitoring, alerting, and documentation processes, and work with Engineering teams to ensure complete feature uptime.
• Grow as a thought leader in the broader PrizePicks technology org, staying abreast of and implementing novel technologies, and disseminating knowledge and best practices to junior members of the team and collaborators alike.

What you have:

• 5+ years of experience in Backend Engineering/Machine Learning Engineering, shipping and maintaining production-grade systems for internal tools and product users.
• 2+ years of experience acting as technical lead and providing mentorship and feedback to junior engineers and scientists.
• Extensive experience exposing real-time predictive model outputs to production-grade systems, leveraging large-scale cloud-based data streaming pipelines and infrastructure.
• Extensive experience working cross-functionally with data engineering, data science, product, and engineering teams, as well as external data providers and 3rd party services.
• Experience in most of the following:
  • SQL/NoSQL databases/warehouses: Postgres, BigQuery, BigTable, 
  • Scripting languages: SQL, Python, Go, Rust.
  • Cloud platform services in GCP and analogous systems: Cloud Storage, Cloud Compute Engine, Cloud Functions, Kubernetes Engine.
  • Code version control: Git
  • Code testing libraries: PyTest, PyUnit, etc
  • Common ML and DL frameworks: scikit-learn, PyTorch, Tensorflow
  • Modeling methods: classical ML techniques, deep learning, gradient boosting, bayesian methods, generative models.
  • MLOps tools: DataBricks, MLFlow, Kubeflow, DVC.
  • Data pipeline and workflow tools: Airflow, Argo Workflows, Cloud Workflows, Cloud Composer, Serverless Framework.
  • Monitoring and Observability platforms: Prometheus, Grafana, Datadog, ELK stack.
  • Infrastructure as Code platforms: Terraform, Google Cloud Deployment Manager.
  • Other platform tools such as Redis, FastAPI, Docker and data visualization tools such as Streamlit or Dash.
• Excellent organizational, communication, presentation, and collaboration experience with organizational technical and non-technical teams
• Graduate degree in Computer Science, Statistics, Mathematics, Informatics, Information Systems or other quantitative field

What makes you stand out:

• Experience building real-time production data science pipelines in a daily fantasy sports or oddsmaking business

Where you’ll live:

• While we prefer candidates based in Atlanta, we are open to quali