Lead Machine Learning Engineer

Root is on a mission to unbreak insurance by creating experiences people love at prices they can’t believe. We believe that investing in world-class technology will facilitate a new class of insurance products, driving a massive positive impact on the hundreds of millions of drivers who carry auto insurance in the US. Root’s Engineering team is committed to building a flexible platform on which our product designers and quantitative scientists can quickly test ideas, deploy them into production, and iterate, with the ultimate objective of a delightful customer experience coupled with effective risk management.

The Opportunity

Price is the most important component of an insurance product, with the ability to drive customer delight through lower prices unlocked by state-of-the-art predictive modeling. The Pricing Platform team owns the foundational technology that powers the R&D and production lifecycle for Root’s most critical machine learning models. This platform is a cornerstone of Root’s strategic goal of becoming the best in the world at pricing and automation.

In this role, you will help build the next generation of Root’s machine learning platform for pricing, creating the infrastructure that allows researchers to move rapidly from experimentation to production. You will work closely with researchers on problems including feature pipelines and feature stores, reproducible model training and orchestration, model registries and versioning, automated validation, model serving, production observability, and tooling that ensures consistency between research and production. You will also explore how emerging LLM technology can revolutionize the data science workflow, improving the way models are developed, tested, deployed, and maintained, with the goal of dramatically reducing the time and effort required to turn new research into production pricing models.

As a Lead Machine Learning Engineer, you are responsible for core team delivery and operations, turning the co-designed architecture into a reliable system through the team’s orchestration of the software development lifecycle. You will also ensure production systems are reliably serving the needs of our customers.

This is a hands-on technical leadership role. You will write and review code, drive execution, and be accountable for the reliability of the systems your team owns.

Salary Range: $164,200 - $240,000 (Eligible for competitive bonus and equity offering)

Root is a “work where it works best” company. This means we will support you working in whatever location that works best for you across the US.

How You Will Make an Impact

• Lead the implementation of the long-term technical roadmap that accelerates pricing innovation through ML tools and workflows that improve the end-to-end pricing R&D process
• Work closely with researchers to define platform needs that improve R&D ergonomics from data readiness through feature engineering, model fitting, serving, diagnostics, and monitoring
• Contribute hands-on to core platform capabilities including feature pipelines and feature stores, reproducible model training and orchestration, model registries and versioning, automated validation, model serving, production observability, and supporting tooling
• Automate end-to-end workflows, leveraging LLM technology to power agentic data science workflow automation
• Own the execution of major platform capabilities by breaking down ambiguous problems, managing dependencies, guiding design decisions, and driving work from design through production
• Orchestrate the software development lifecycle across the team and mentor and grow engineers through technical guidance and feedback
• Ensure standards for reliability, observability, reproducibility, and correctness are met across the platform’s systems

• 8+ years of software engineering experience, with a demonstrated track record of building and delivering production ML platforms and large-scale data processing systems
• Hands-on experience building core ML platform capabilities such as feature pipelines or feature stores, reproducible model training and orchestration, model registries and versioning, automated validation, or model serving
• Strong system design and distributed systems fundamentals, with experience building reliable, scalable production services and data pipelines
• Working knowledge of the ML lifecycle and the engineering considerations involved in training, evaluating, deploying, and operating models in production
• Experience designing and operating systems with strong requirements around reliability, observability, reproducibility, and correctness
• Demonstrated ability to take ambiguous technical initiatives, break them into executable work, manage dependencies, and drive delivery across multiple engineers
• Strong track record of working closely with Data Scientists or researchers to translate research needs into production platform capabilities
• Experience mentoring engineers, guiding technical design, and raising the engineering quality of a team
• Proficiency with Python and modern ML and data tooling
• Excellent communication skills with engineers, researchers, Product, and cross-functional stakeholders

Preferred Qualifications

• Understanding of ML models, including model inputs, assumptions, evaluation methodology, tradeoffs, and typical failure modes in production
• Experience improving research velocity, experimentation throughput, deployment speed, or developer productivity through ML-platform investments
• Experience solving training-serving consistency, model lineage, reproducibility, or model-versioning challenges at scale
• Experience building ML platforms in a regulated or highly data-intensive environment such as insurance, fintech, financial services, or healthcare
• Experience applying LLMs or agentic systems to developer tooling, research workflows, data science automation, or other internal productivity use cases