Root

Lead Machine Learning Engineer, Lifetime Value

Remote (United States)

Sponsorship not specified$164k-$205kDetected 55 days ago

PythonSnowflakeDatabricksAWSGCPMachine LearningSparkAirflowdbtData ScienceMLOpsStatisticsA/B TestingForecastingResearchCommunicationMentoring

About the role

At Root, we are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.
You will help accelerate the path from experimentation to production while improving the reliability and operational maturity of Root's ML ecosystem.

You will help shape the foundations that enable statistical models, simulations, and forecasts to drive measurable business impact across the organization.
Partner with data scientists to productionize statistical models, simulations, and forecasting workflows that support decision-making across the business.
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.
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.
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.
This helps us create a more personal and engaging experience for both you and our interviewers.

The Opportunity We believe that a disruptive insurance company must have a principled quantitative framework at its foundation.
We believe that a disruptive insurance company must have a principled quantitative framework at its foundation.
Experience with cloud-based ML infrastructure and data platforms such as AWS, GCP, or Azure.
Experience with infrastructure as code, such as Terraform.

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.
If you have any concerns, feel free to let us know once you are contacted.
We're happy to talk it through.
The policy regarding requests for reasonable accommodation applies to all aspects of the hiring process.
If reasonable accommodation is needed, please contact recruiting@joinroot.com.

$164,000 - $205,000 (Eligible for Competitive Bonus & Equity Offering) How You Will Make an Impact Build and improve the systems that power customer lifetime value modeling, from development and deployment through monitoring and production support.

Root is seeking a Lead Machine Learning Engineer I to help build the systems and workflows that power our customer lifetime value modeling ecosystem.
5+ years of experience designing, building, deploying, and maintaining machine learning systems and ML model pipelines in partnership with data scientists.

This listing is sourced directly from Root's careers page and normalized into a canonical job model.