Staff Machine Learning Engineer, Pricing Platform Root Insurance · Remote · US · Machine Learning Engineering $188,800–$265,000 5w ago

Aimlroles; Northern (KY); Hybrid; USD 189,000 - 265,000; Full time

Job summary

Root is reimagining insurance pricing by building a next‑gen ML platform that enables researchers to move rapidly from experimentation to production. As Staff Machine Learning Engineer, you will own long‑term architectural design, versioned contracts between data, features, models, and production pricing, and drive the technical strategy for deployable, self‑serve tooling.

You will work across platform teams and with Data Science and Actuarial to ensure reproducibility and strong production

Qualifications

• 10+ years of software engineering experience with a track record of designing and delivering ML platforms
• Architectural ownership of production ML infrastructure including feature pipelines/models serving
• Strong system design and distributed systems expertise with scalable data processing
• Experience ensuring reproducibility, lineage, versioning and production correctness
• Familiarity with ML lifecycle from training to deployment

Responsibilities

• Define long-term technical roadmap to accelerate pricing innovation with ML tools and workflows
• Collaborate with researchers to translate platform needs into scalable capabilities
• Define architecture and versioned contracts linking data, features, models and production pricing
• Automate end-to-end workflows and explore LLM-based automation
• Write and review code in critical platform components and guide cross-team design
• Mentor senior engineers in architecture and ML engineering best practices
• Set reliability, observability, reproducibility and correctness standards across the platform

Skills

ML platform design

Distributed systems

Python

Team leadership

Tools

Python

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

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