Job description

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 Staff Machine Learning Engineer , you are the technical leader of the team, owning long-term architectural design of the platform, the versioned contracts between data, features, models, and pricing, and the technical strategy that makes pricing models deployable with self-serve technology. Your influence is cross-team, spanning platform teams, their dependencies, and the interface with Data Science and Actuarial. This is a hands-on role. You design, you write and review code in the most critical parts of the system, and you are accountable for the technical coherence of the platform over time. Salary Range : $188,800 - $265,000 (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 • Define the long-term technical roadmap that accelerates pricing innovation through ML tools and workflows that improve the end-to-end pricing R&D process, balancing iterative delivery with the long-term vision for the platform • 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 • Define the architecture and versioned contracts connecting data, features, models, and production pricing, ensuring the platform remains reproducible and technically coherent as it evolves • Automate end-to-end workflows, leveraging LLM technology to power agentic data science workflow automation • Write and review code in the most critical parts of the platform and drive technical design across systems and cross-team dependencies • Mentor senior engineers in architecture, platform design, and best practices in ML engineering • Set standards for reliability, observability, reproducibility, and correctness across the platform’s systems What You Will Need to Succeed • 10+ years of software engineering experience, with a demonstrated track record of designing and delivering business-critical ML platforms, data platforms, or similarly complex distributed systems • Demonstrated architectural ownership of production ML infrastructure, including systems such as feature pipelines or feature stores, model training and orchestration, model registries and versioning, model serving, or research-to-production infrastructure • Strong system design and distributed systems expertise, including experience designing reliable, scalable data processing systems and well-defined interfaces between complex systems • Experience designing systems that provide strong guarantees around reproducibility, lineage, versioning, training-serving consistency, and production correctness • Working knowledge of the ML lifecycle and the engineering considerations involved in training, evaluating, deploying, and operating models in production • Demonstrated ability to establish technical direction in ambiguous problem spaces and translate long-term architectural goals into incremental, executable plans • A track record of creating technical leverage across multiple teams through shared platforms, abstractions, standards, or tooling • Demonstrated ability to influence technical direction across teams without direct authority and mentor senior engineers on architecture and system design • Strong experience collaborating with Data Scientists and researchers to understand research workflows and translate their needs into scalable platform capabilities • Proficiency with Python and modern ML and data tooling • Excellent written and verbal communication skills, with the ability to communicate effectively across Engineering, Data Science, Product, and leadership Preferred Qualifications • Understanding of ML and statistical modeling, including model assumptions, bias and variance, uncertainty, evaluation methodology, and common model failure modes • Understanding of how common ML algorithms and frameworks operate beneath their interfaces and how those details influence production system design • Experience improving model development velocity, experimentation throughput, deployment reliability, or model quality through ML-platform investments • Experience building ML infrastructure in a regulated or highly data-intensive domain such as insurance, fintech, financial services, or healthcare • Experience designing platforms used by quantitative researchers or Data Scientists with demanding experimentation and reproducibility requirements • Experience applying LLMs or agentic systems to developer tooling, research workflows, or data science automation 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. Please see our Privacy Notice available HERE for more information on how we process your personal data. 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 recruiting@joinroot.com.

Requirements

Must-haves

This is a hands-on role. You design, you write and review code in the most critical parts of the system, and you are accountable for the technical coherence of the platform over time.
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
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
Define the architecture and versioned contracts connecting data, features, models, and production pricing, ensuring the platform remains reproducible and technically coherent as it evolves
Automate end-to-end workflows, leveraging LLM technology to power agentic data science workflow automation
Write and review code in the most critical parts of the platform and drive technical design across systems and cross-team dependencies
Mentor senior engineers in architecture, platform design, and best practices in ML engineering
Set standards for reliability, observability, reproducibility, and correctness across the platform’s systems
What You Will Need to Succeed
10+ years of software engineering experience, with a demonstrated track record of designing and delivering business-critical ML platforms, data platforms, or similarly complex distributed systems
Strong system design and distributed systems expertise, including experience designing reliable, scalable data processing systems and well-defined interfaces between complex systems
Experience designing systems that provide strong guarantees around reproducibility, lineage, versioning, training-serving consistency, and production correctness
Working knowledge of the ML lifecycle and the engineering considerations involved in training, evaluating, deploying, and operating models in production
Demonstrated ability to establish technical direction in ambiguous problem spaces and translate long-term architectural goals into incremental, executable plans
A track record of creating technical leverage across multiple teams through shared platforms, abstractions, standards, or tooling
Demonstrated ability to influence technical direction across teams without direct authority and mentor senior engineers on architecture and system design

Nice-to-haves

Understanding of ML and statistical modeling, including model assumptions, bias and variance, uncertainty, evaluation methodology, and common model failure modes
Understanding of how common ML algorithms and frameworks operate beneath their interfaces and how those details influence production system design
Experience improving model development velocity, experimentation throughput, deployment reliability, or model quality through ML-platform investments
Experience building ML infrastructure in a regulated or highly data-intensive domain such as insurance, fintech, financial services, or healthcare
Experience designing platforms used by quantitative researchers or Data Scientists with demanding experimentation and reproducibility requirements
Experience applying LLMs or agentic systems to developer tooling, research workflows, or data science automation
Please see our Privacy Notice available HERE for more information on how we process your personal data.

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Keywords to mirror

machine learningllmmlpython

Platform: rippling
Effort: medium

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