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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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.
