Teams at Lyft

Design and analyze experiments, develop statistical and machine learning models, identify patterns in large datasets, build data pipelines, establish product health metrics, and translate insights into decisions. The role partners closely with product, engineering, operations, and other data science teams to improve Lyft payment experiences, payout features, earnings transparency, and platform integrity.

Build and maintain backend features for Lyft’s Growth Platforms, including incentives and messaging systems. Responsibilities include designing distributed services, writing reliable and tested code, debugging production issues, improving system performance and observability, participating in code reviews and on-call rotations, and collaborating with product, design, data science, and engineering teams. The role requires 3–5 years of backend software engineering experience and familiarity with cloud infrastructure, databases, APIs, asynchronous processing, and CI/CD.

Leads 6–8 engineers building and operating Lyft’s high-scale dynamic pricing and ride offer selection systems. Owns roadmap execution, architecture, model productionization, reliability, SLAs, incident escalation, technical debt, hiring, performance management, and team development. Partners with Product, Science, Analytics, and senior stakeholders to balance technical, product, and business priorities.