Top Tech Jobs & Startup Jobs in Los Angeles, CA

Leads engineering for one or more product-domain teams, partnering with Engineering Managers, Product, Data Science, Analytics, and Insurance stakeholders. Responsibilities include setting technical strategy and roadmaps, coaching engineers, driving cross-team initiatives, contributing code, leading incident response and resiliency improvements, and establishing engineering standards. Requires strong Ruby on Rails expertise, Rust familiarity, software leadership experience, and excellent communication skills.

Leads Root’s SEC and US GAAP financial reporting, technical accounting policy, disclosure controls, audit coordination, and accounting standards implementation. Oversees complex accounting analyses involving insurance, investments, debt, leases, and statutory reporting. Partners with cross-functional leaders, auditors, regulators, and investors while improving reporting systems and close processes. Manages and develops the financial reporting team and prepares Audit Committee and Board materials.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.