登录获取应用

职位 › 职位详情

Software Engineer Intern, Backend (Summer 2027 - Mexico)

Lyft · Mexico City, Mexico

实习现场办公英语

职位介绍

摘自雇主发布的职位信息 · Lyft · 发布于2026年9月17日

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Interns work side-by-side with top engineers in the industry while having autonomy from the get-go. They contribute to user-facing products and are able to see their work go live quickly. Lyft fosters a collaborative environment in the office, so there's always a sharp mind eager to hear about your next idea. So what's yours?

Responsibilities:

  • Own your project, while checking in with other team members throughout the day with questions and updates
  • You leave the code in a better state than when you found it (progressive refactor)
  • You value reliability, ensured by testing (unit, integration and load tests)
  • Participate in code reviews to ensure code quality and distribute knowledge
  • Continuous integration and deployment
  • Go home knowing that your work today is meaningfully improving the lives of every Lyft driver and every Lyft passenger!

Experience:

  • Currently pursuing a Bachelor's or Master's degree in Computer Science at a university in Mexico (required), with a graduation date between December 2027 and Summer 2028 (required). For any candidates who are master's students who worked between their bachelor's and master's programs: candidates should have less than 2 years of relevant full-time work experience
  • Available during Summer 2027 for an internship in Mexico City
  • Strong knowledge of CS fundamentals
  • Understanding of unit, integration, and end-to-end testing
  • Excellent communication skills
  • Passion for community, sustainability, and/or transportation
  • <span style="font-weight: 400;

提及的技能

University

每个职位,都有你的匹配分

BabZituna按六个真实维度,将每个职位与你的资料对照评分,并告诉你为什么得出这个分数,公平性经过审计(阅读公开的偏见审计)。

获取应用 → ✓ 求职者100%免费
一个职位如何评分 示例
技能96经验90地点84工作方式74工作类型61薪资无数据

示例数据,并非真实候选人。每个维度根据你本人的资料按100分制评分;无法衡量的维度会如实标明,而不是猜测。