Key skills
Machine learning systems designMachine learning model deploymentMachine learning model monitoringMachine learning workflow orchestrationPython programmingSoftware engineering fundamentalsCloud-based ML infrastructureAWSGCPAzureInfrastructure as codeTerraformPythonMachine LearningMLMLflowSnowflakeDatabricksSparkAirflowdbtCommunication
About this role
Root Inc. is a disruptive insurance company focused on improving customer experiences through innovative insurance solutions. They are seeking a Lead Machine Learning Engineer to build scalable machine learning systems that support high-impact decisions across various business functions, enhancing the reliability and operational maturity of their ML ecosystem.
Responsibilities:
- Build and improve the systems that power customer lifetime value modeling, from development and deployment through monitoring and production support
- Partner with data scientists to productionize statistical models, simulations, and forecasting workflows that support decision-making across the business
- Accelerate the path from research to production through scalable infrastructure, reliable workflows, and reusable tooling
- Improve the ML development experience by building better operational patterns and advancing production-ready ML practices
- Develop tools and services that help stakeholders evaluate model performance, understand business impact, and trust model outputs in production
- Collaborate with technical and business partners to solve high-value problems and improve the reliability and scalability of ML systems
- Share best practices through mentorship, documentation, and clear communication around technical decisions, tradeoffs, and operational considerations
Requirements:
- BS in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
- 5+ years of experience designing, building, deploying, and maintaining machine learning systems and ML model pipelines in partnership with data scientists
- Strong Python and software engineering fundamentals, with the ability to build maintainable ML systems and production-quality code
- Experience building and operating production ML systems, including deployment, monitoring, debugging, and workflow orchestration
- Ability to design reproducible systems with clear lineage, versioning, and operational visibility across complex ML workflows
- Comfort working in ML systems with interconnected components, simulation-driven logic, and embedded business rules
- Strong judgment around model evaluation, code quality, system reliability, and maintainable engineering tradeoffs
- Experience with cloud-based ML infrastructure and data platforms such as AWS, GCP, or Azure
- Experience with infrastructure as code, such as Terraform
- Clear communication skills and the ability to explain technical tradeoffs to both technical and non-technical audiences
- MS or PhD in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
- Familiarity with customer lifetime value forecasting, simulation workflows, or Forecast vs. Actual analysis
- Experience with insurance or regulated financial products
- Exposure to ML and data tooling, orchestrators, and platforms such as MLflow, Airflow, Dagster, Snowflake, Databricks, dbt, and Spark
- Experience building shared ML infrastructure, developer tooling, or reusable systems that improve data science productivity