Note: The job is a remote job and is open to candidates in USA. Root Inc. is an insurance technology company focused on improving auto insurance through advanced technology and predictive modeling. The Staff Machine Learning Engineer will lead the architecture and development of a machine learning platform that enables researchers to move pricing models from experimentation into production. The role involves building reproducible workflows, feature and model infrastructure, automated validation and serving, observability, and LLM-powered data science tooling.
Responsibilities
- 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
Skills
- 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
- 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
Benefits
- 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.
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