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Backend software engineering internship focused on owning projects, writing reliable and tested code, participating in code reviews, refactoring, and supporting continuous integration and deployment. Interns collaborate with experienced engineers and contribute to user-facing Lyft products. The role requires strong computer science fundamentals, database experience, familiarity with real-time technology and mobile development, and availability for a Summer 2027 hybrid internship in San Francisco.

Lead and grow a firmware engineering team developing embedded systems for e-bikes, scooters, batteries, docks, and charging infrastructure. Set technical direction for IoT, powertrain, battery management, OTA updates, diagnostics, safety, and fleet operations. Manage roadmaps, staffing, releases, hiring, and performance. Partner cross-functionally on product development, regulatory certification, reliability, and sustaining engineering while establishing CI/CD, HIL testing, code review, and incident response practices.

Data Science Interns will develop optimization, machine learning, or inference models supporting Lyft’s marketplace and transportation platform. Responsibilities include exploratory data analysis, production modeling, algorithm implementation, simulated and live experiments, statistical analysis, and communicating findings to guide launch decisions. Interns collaborate with engineers, product managers, and cross-functional partners. Candidates must be pursuing a relevant master’s or PhD and be available in Summer 2027 for a hybrid internship in New York City.

Data Science Interns develop mathematical, optimization, machine learning, and statistical models for Lyft’s marketplace and transportation platform. Responsibilities include exploratory data analysis, production modeling, algorithm implementation, simulated and live experiments, experimental analysis, and communicating findings to support launch decisions. Interns collaborate with engineers, product managers, and cross-functional teams.

Manage outside counsel and vendor billing across eBilling and matter management platforms. Review invoices, enforce billing guidelines and fee arrangements, maintain rate cards and budgets, resolve billing disputes, support accruals and reconciliations, onboard vendors, and develop legal spend metrics. The role also documents workflows, validates reports, supports platform improvements, and contributes to broader Legal Operations initiatives.

Designs, builds, and integrates ride-experience platforms and microservices. Develops and improves APIs, performance, monitoring, testing, validation, and documentation. Troubleshoots application and platform issues, contributes to architecture and technology roadmaps, and manages project priorities, deadlines, and deliverables.

Design, develop, deploy, monitor, operate, and maintain scalable systems and infrastructure. Collaborate with engineering, science, and product teams to drive projects from inception through launch. Write tested, maintainable code, participate in code reviews, and build highly available detection and response platforms supporting security monitoring and threat identification.

Lead strategic sourcing and end-to-end procurement across indirect technology categories. Manage supplier relationships, negotiate complex contracts, deliver cost savings, develop category strategies, and improve sourcing processes through technology and data analytics. Responsibilities include market analysis, RFx development, supplier evaluation, stakeholder management, contract negotiation, and overseeing multiple projects.

Partners with engineering, product, and business teams to frame problems, perform exploratory analysis, build statistical and machine learning models, write production code, run experiments, analyze data, communicate findings, and support launch decisions.

Design, develop, deploy, and improve real-time machine learning systems for Lyft’s Fulfillment team. Build feature pipelines, training workflows, and model-serving infrastructure; translate research into production; evaluate models against business KPIs; run experiments; write production code; conduct code reviews; and collaborate with product, data science, and engineering teams.