Machine Learning Engineer

Summary

Join PrizePicks, the fastest-growing sports company in North America, as a Data Science/Machine Learning Engineer. You will be developing, maintaining, and leading projects related to streaming data and MLOps infrastructures for real-time, simulation-based market pricing. Collaborate with Data Science to operationalize machine learning assets, ensuring model quality and scalability. Design, implement, and deploy the data, MLOps, and API stack for real-time pricing models and other critical functions. Partner with cross-functional teams to create and distribute real-time data products. Build and maintain monitoring and documentation processes, and grow as a thought leader within the organization. This role requires 3+ years of experience in backend engineering or machine learning engineering and a graduate degree in a quantitative field.

Requirements

• 3+ years of experience in Backend Engineering/Machine Learning Engineering, shipping and maintaining production-grade systems for internal tools and product users
• Experience exposing real-time predictive model outputs to production-grade systems, leveraging large-scale cloud-based data streaming pipelines and infrastructure
• 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

Responsibilities

• 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

Preferred Qualifications

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

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