Lead Machine Learning Engineer, Lifetime Value

OhioX; Northern (KY); Hybrid; USD 164,000 - 205,000; Full time

Job summary

Root is hiring a Lead Machine Learning Engineer I to develop and operate scalable ML systems across Marketing, Finance, Product and Customer Experience. You’ll work with data scientists and engineers to productionize models, simulations, and forecasting workflows to drive business decisions.

The role focuses on building infrastructure, tooling and patterns enabling ML at scale, with strong emphasis on reliability and operational maturity.

Qualifications

• 5+ years designing and deploying ML systems with data scientists.
• Strong Python and production-grade software skills.
• Experience with cloud ML infrastructure and CI/CD patterns.

Responsibilities

• Build and scale ML systems and pipelines across the business.
• Partner with data scientists to productionize models and forecasts.
• Improve reliability, observability, and operational patterns of ML workflows.
• Mentor teams and share best practices for production ML.

Skills

5+ years ML systems

Python

Production ML

Cloud platforms

Infrastructure as code

Education

BS in Statistics/Mathematics/Engineering

Tools

MLflow

Airflow

Terraform

Job description

At Root, we’re on a mission to improve the lives of our customers by offering better insurance solutions. We challenge ourselves to think differently in order to reimagine insurance to make it smarter, more equitable, and a better experience for all.

We strive to “unbreak” the archaic insurance industry by using data and technology in innovative new ways. We believe we must be steadfast in our commitments to research, experimentation, and disciplined data-driven decision making in order to build products our customers love.

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.

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