Note: The job is a remote job and is open to candidates in USA. Root Inc. is an insurance technology company focused on creating better customer experiences and more affordable auto insurance through advanced technology. The Lead Machine Learning Engineer will build and operate the machine learning platform powering pricing research and production, including feature pipelines, model training, serving, validation, observability, and LLM-enabled workflow automation. The role provides hands-on technical leadership, drives platform delivery, and mentors engineers.
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
Skills
- 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
- 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
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.
Company Overview
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