Jobs at Lyft

Contribute to designing, training, testing, and deploying machine learning models and production pipelines. Collaborate with product managers, data scientists, and ML engineers to solve business problems, analyze data, communicate findings, and participate in code and specification reviews. The intern will apply research papers in working code, build efficient ML workflows, and develop high-quality software in a collaborative environment.

Conduct qualitative and quantitative UX research for Lyft drivers and riders using interviews, usability testing, field research, surveys, diary studies, and other methods. Analyze research data, develop actionable insights, and communicate recommendations that guide product and design improvements. Collaborate with Design, Product Management, Analytics, Engineering, and senior researchers while gaining mentorship and contributing to user-facing products.

Backend software engineering intern responsible for owning projects, writing and improving code, testing for reliability, participating in code reviews, and supporting continuous integration and deployment. The role requires strong computer science fundamentals, database experience, real-time technology problem-solving, and potential mobile development experience. The internship is based in Toronto during Summer 2027 with a hybrid in-office schedule.

Data Engineer Intern supporting large-scale data projects at Lyft. Responsibilities include owning an internship project, improving code quality, writing tests, participating in code reviews, and contributing to continuous integration and deployment. The role requires database experience, SQL and data modeling knowledge, strong computer science fundamentals, and effective communication. This hybrid Toronto internship is available during Summer 2027.

Software Engineer Intern working on full-stack, user-facing Lyft products. Responsibilities include developing and refactoring code, writing unit, integration, and load tests, participating in code reviews, and supporting continuous integration and deployment. Candidates should have web development experience with Python, JavaScript, React, TypeScript, databases, testing tools, browser technologies, and Git. The internship is based in Toronto during Summer 2027 on a hybrid schedule.

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

Build and support enterprise integrations and automations using Workato and Boomi, with emphasis on Legal systems. Design APIs, SFTP connections, ETL pipelines, AI agents, MCP servers, custom scripts, and employee-facing workflow tools. Manage secure platform environments, troubleshoot production issues, and integrate CRM, HR, Finance, Supply Chain, and Legal applications. Partner with stakeholders to gather requirements and provide production support and on-call coverage.

Build and operate large-scale infrastructure and data pipeline platforms. Responsibilities include developing automation, monitoring system health, improving reliability and scalability, managing cloud infrastructure, participating in design and production readiness reviews, planning capacity, documenting processes, and responding to incidents through on-call rotations. The role requires production software development, infrastructure tooling, data streaming and storage expertise, and experience optimizing distributed systems in public cloud environments.

Owns outside counsel and vendor billing operations, including invoice review, eBilling platform administration, guideline compliance, rate approvals, accruals, reconciliations, and dispute resolution. Partners with Legal Operations, Finance, attorneys, and law firms to maintain accurate legal spend. Develops spend analytics, dashboards, workflows, and playbooks while supporting platform improvements and broader legal operations initiatives.

Develop and maintain backend software for Lyft’s claims technology platform. Own features from product specifications through development, deployment, and maintenance; write tested, maintainable code; participate in code reviews; design scalable team-owned components; document technical designs; and collaborate with stakeholders and insurance partners to improve claims operations and reduce risk and costs.

Own Lyft’s B2B marketing technology stack, architecture, roadmap, integrations, data model, lead lifecycle, tracking, attribution, governance, reliability, and budget. Configure HubSpot, integrate CRM and data systems, improve workflows with AI, ensure privacy and deliverability compliance, lead cross-functional programs, provide technical direction, mentor support teams, and communicate strategy and risks to leadership.

Develop and deploy algorithmic and machine learning solutions for Lyft’s mapping products, improving route recommendations, traffic prediction, and travel-time estimates. Responsibilities include data exploration, feature engineering, experimentation, production ML systems, real-time inference, batch pipelines, monitoring, optimization, and metric development. The role partners with Engineering, Product, Operations, and Science teams to translate complex mobility and geospatial challenges into scalable solutions.

Lead analytics for Lyft’s airport marketplace by analyzing pricing, earnings, supply, cancellations, ride modes, and airport performance. Develop metrics, dashboards, models, reporting, and reusable analytical frameworks to identify growth and efficiency opportunities. Partner with product, engineering, marketing, design, and operations teams to turn insights into product decisions, and present recommendations and results to senior leadership and cross-functional stakeholders.

Use data science, experimentation, forecasting, and analytical frameworks to improve Lyft’s airport marketplace. Analyze large datasets, define product health metrics, identify growth opportunities, recommend technical and product solutions, and measure feature impact. Collaborate with engineering, product, design, and other scientists while presenting actionable findings to senior leadership and cross-functional stakeholders.

Develop backend Trust and Safety tools, features, and distributed systems that protect Lyft riders and drivers. Lead safety features from concept through launch, collaborate with product, design, policy, and engineering partners, write maintainable and tested code, participate in reviews and on-call rotations, and promote engineering best practices through knowledge sharing.

Lead the architecture and technical direction of Lyft’s mapping data platforms, including offline experimentation tooling, route simulation services, scalable pipelines, data governance, and observability. Own pipeline reliability and SLAs, evolve schemas, optimize SQL and data processing, build backend services and AI-assisted data tools, and mentor engineers. Collaborate with engineering, product, machine learning, and data science teams to improve routing quality and travel-time accuracy.

Build and launch reliable, scalable pricing and rider-engagement systems for Lyft’s marketplace. Responsibilities include leading projects and features, developing tested backend code in Python or Go on AWS, solving distributed systems and concurrency challenges, collaborating cross-functionally, participating in code reviews and on-call rotations, and promoting engineering best practices. The role may also involve real-time machine learning serving and experimentation systems.

Build backend infrastructure and APIs to integrate Lyft with emerging AI ecosystems and devices. Design scalable distributed systems, mentor engineers, drive high-impact projects, write and review maintainable code, participate in on-call rotation, and collaborate cross-functionally to deliver production-grade AI-enabled features.

Lead and integrate Knowledge Management, Quality, Learning & Performance, and Tooling Enablement to scale Lyft’s customer support. Set vision and OKRs, drive AI-native adoption, partner with Product/Engineering/Operations, remove execution blockers, and build high-performing teams to improve customer outcomes.

Lead analytics for Safety & Customer Care by partnering cross-functionally to define metrics, build dashboards, design and analyze A/B tests, monitor KPIs, and deliver data-driven recommendations to improve customer support experience and inform senior leadership.