Lead Machine Learning Engineer

Root Insurance

8d•$164,200 - $240,000•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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