The Opportunity

We believe that a disruptive insurance company must have a principled quantitative framework at its foundation. At Root, we are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.

Root is seeking a Lead Machine Learning Engineer I to help build the systems and workflows that power our customer lifetime value modeling ecosystem.

In this role, you will partner closely with data scientists, engineers, and business teams to build scalable machine learning systems that support high-impact decision-making across Marketing, Finance, Product, and Customer Experience. You will help accelerate the path from experimentation to production while improving the reliability and operational maturity of Root's ML ecosystem.

This role focuses on building the infrastructure, tooling, and operational patterns that allow machine learning systems to scale reliably in production. You will help shape the foundations that enable statistical models, simulations, and forecasts to drive measurable business impact across the organization.

The ideal candidate is a machine learning engineer who enjoys building high-leverage systems, improving how technical teams work, and enabling machine learning to operate reliably at scale.

Root is a "work where it works best" company, meaning we will support you working in whatever location works best for you across the U.S.

Salary Range: $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.
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.

What You Will Need to Succeed

BS in 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.

Nice to Have

MS or PhD in 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.

As part of Root's interview process, we kindly ask that all candidates be on camera for virtual interviews. This helps us create a more personal and engaging experience for both you and our interviewers. Being on camera is a standard requirement for our process and part of how we assess fit and communication style, so we do require it to move forward with any applicant's candidacy. If you have any concerns, feel free to let us know once you are contacted. We're happy to talk it through.

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Lead Machine Learning Engineer, Lifetime Value