[Hiring] Lead Machine Learning Engineer I @Root Insurance

Role Description

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.
• Help accelerate the path from experimentation to production while improving the reliability and operational maturity of Root’s ML ecosystem.
• Focus on building the infrastructure, tooling, and operational patterns that allow machine learning systems to scale reliably in production.
• 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)

Qualifications

• 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.

Nice to Have

• 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.

Requirements

• 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.

Company Description

• Consistent with the Americans with Disabilities Act (ADA) and the Civil Rights Act of 1964, it is the policy of Root to provide reasonable accommodation when requested by a qualified applicant or candidate with a disability, unless such accommodation would cause an undue hardship for Root.
• The policy regarding requests for reasonable accommodation applies to all aspects of the hiring process.
• If reasonable accommodation is needed, please contact [email protected] .