Lead Machine Learning Engineer

Root is working to transform the insurance industry by creating smarter, fairer, and more customer-focused insurance solutions. The company combines technology, data, research, and experimentation to rethink traditional insurance practices and deliver products that provide a better experience for customers.

Data-driven decision-making is central to Root’s approach. The company continuously explores modern technologies and analytical methods to improve its products, operations, and understanding of customer needs.

The Opportunity

Root is hiring a Lead Machine Learning Engineer I to help develop and strengthen the systems, infrastructure, and workflows supporting its customer lifetime value modeling ecosystem.

The position involves working closely with data scientists, software engineers, and business teams to create scalable machine learning solutions that support important decisions across Marketing, Finance, Product, and Customer Experience.

A major focus of the role will be reducing the gap between machine learning experimentation and production. You will develop infrastructure, reusable tools, and operational processes that make ML systems more reliable, scalable, observable, and easier to maintain.

You will also contribute to the technical foundations that allow statistical models, simulations, and forecasting systems to generate measurable business value throughout the organization.

This opportunity is well suited to an experienced machine learning engineer who enjoys developing high-impact technical systems, improving engineering workflows, and enabling data science teams to operate machine learning solutions reliably at scale.

Root operates under a “work where it works best” philosophy, allowing eligible employees to work from locations across the United States that best suit their needs.

How You’ll Make an Impact

• Develop and enhance systems supporting customer lifetime value modeling, covering development, deployment, monitoring, maintenance, and production support.
• Collaborate with data scientists to transform statistical models, simulations, and forecasting workflows into dependable production systems.
• Shorten the journey from research and experimentation to production by developing scalable infrastructure, reusable tools, and reliable workflows.
• Improve the machine learning development experience by establishing effective operational standards and production-ready ML practices.
• Build services and tools that allow stakeholders to assess model performance, understand business outcomes, and confidently use model outputs.
• Work with engineering, data, and business teams to address high-value challenges while improving ML system scalability and reliability.
• Promote strong engineering practices through documentation, mentorship, knowledge sharing, and clear explanations of technical decisions and trade-offs.

What You’ll Need to Succeed

• Bachelor’s degree in Statistics, Mathematics, Engineering, or another relevant quantitative discipline.
• At least 5 years of professional experience designing, developing, deploying, and maintaining machine learning systems and ML pipelines in collaboration with data scientists.
• Strong Python programming skills and solid software engineering fundamentals.
• Experience developing maintainable, production-quality machine learning applications and infrastructure.
• Practical knowledge of production ML operations, including deployment, monitoring, debugging, and workflow orchestration.
• Ability to create reproducible ML systems with effective version control, lineage tracking, and operational visibility.
• Experience working with complex machine learning environments involving interconnected components, simulations, and business logic.
• Sound judgment regarding model evaluation, code quality, system reliability, maintainability, and engineering trade-offs.
• Experience working with cloud-based machine learning and data infrastructure such as AWS, Google Cloud Platform, or Microsoft Azure.
• Familiarity with Infrastructure as Code technologies such as Terraform.
• Excellent communication skills with the ability to explain technical decisions and trade-offs to both technical and non-technical stakeholders.

Preferred Qualifications

Additional qualifications that would be beneficial include:

• Master’s degree or PhD in Statistics, Mathematics, Engineering, or another quantitative discipline.
• Familiarity with customer lifetime value forecasting, simulation-based workflows, or Forecast vs. Actual analysis.
• Previous experience in insurance, financial services, or other regulated financial industries.
• Experience with machine learning and data technologies such as MLflow, Airflow, Dagster, Snowflake, Databricks, dbt, or Apache Spark.
• Experience developing shared ML infrastructure, internal developer tools, or reusable platforms that improve data science productivity.

Interview Process

As part of Root’s recruitment process, candidates participating in virtual interviews are expected to have their cameras enabled. Root uses video interviews to provide a more interactive experience and as part of its assessment of communication and candidate fit.

Candidates who have concerns regarding this requirement can discuss them with the recruiting team after being contacted.