Top Tech Jobs & Startup Jobs in Los Angeles, CA

Leads engineering within a product domain, driving large-scale strategic initiatives across multiple teams. Establishes technical standards, architecture, and scalable best practices; aligns engineering, product, data science, and business stakeholders; guides roadmaps and resource allocation; communicates with executives; and coaches engineering leaders and senior individual contributors.

Lead complex actuarial pricing work for personal lines insurance, including rate indications, regulatory filings, pricing analyses, predictive-model implementation, and state expansion. Develop automation and scalable filing tools, communicate recommendations to technical and executive stakeholders, manage regulatory risks, and coach other actuaries. Partner cross-functionally with Product, Regulatory, Data Science, Engineering, and Finance teams while maintaining analytical rigor and compliance.

Own analytics for Root’s Customer Experience domain and policyholder retention. Develop metrics, analytical frameworks, experimentation systems, and strategic recommendations. Partner cross-functionally with Product, Engineering, Data Science, and Operations to shape roadmaps, evaluate customer initiatives, quantify business impact, and influence senior leadership decisions through rigorous analysis and clear communication.

Build and guide data platform capabilities supporting marketing, finance, analytics, and data science. Develop data collection, integration, scoring, experimentation, analytics, and infrastructure solutions; contribute code, lead cross-functional technical initiatives, improve system resiliency through incident response, and establish engineering standards. Partner with business and technical stakeholders to define strategy and long-term roadmaps.

Lead technical strategy and execution for build, test, release, and deployment systems. Drive initiatives to improve developer productivity, deployment safety, and platform reliability. Coordinate cross-team projects and migrations, contribute high-impact code, coach engineers, and lead incident response and resilience improvements for shared engineering systems.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.

Set technical direction for Root’s AI platform, including infrastructure, tooling, shared services, governance, reliability, security, observability, and cost management. Lead cross-team initiatives, guide AI and LLM architecture decisions, establish operating standards, shape technical roadmaps, resolve complex tradeoffs, and mentor engineers and technical leaders across the organization.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Own the vision, strategy, and roadmap for Root’s pricing platform and machine learning infrastructure. Lead engineering and partner with data science, actuarial, and engineering teams to improve pricing workflows, model development, deployment, monitoring, and regulatory operations. Drive self-service tooling, LLM-enabled data science workflows, platform reliability, and nationwide expansion while translating business needs into product requirements and prioritized investments.

Lead the AI Platform team in building reliable, secure, observable infrastructure, shared services, deployment patterns, and operational practices for AI capabilities. Set technical priorities, coordinate cross-team dependencies, guide architecture and production operations, improve developer experience, and coach engineers while contributing to complex technical work.