🚘 Uber Frontend Interview Experience — A Deep Dive

Overview

This document details a candidate's comprehensive interview experience for a frontend engineering position at Uber. The process encompassed online assessments, machine coding exercises, system design challenges, and behavioral interviews. The evaluation aimed to assess not only technical proficiency but also problem-solving abilities, collaboration skills, and cultural fit.

Interview Rounds

Round 1: Online Screening Test (CodeSignal)

The initial stage involved an online assessment administered via CodeSignal. This round included:

• Two coding problems: Easy to medium-level algorithmic challenges focusing on arrays and graphs.
• Eight multiple-choice questions: Covering JavaScript fundamentals, web performance optimization, HTML semantics, and browser behavior.

This round served as a preliminary filter, gauging both practical coding abilities and theoretical knowledge.

Round 2: Machine Coding Interview

This round focused on practical coding skills and a deep understanding of JavaScript and browser internals.

• Part 1: Discussion of Past Projects

  • The interviewer inquired about performance optimizations implemented in previous projects, such as lazy loading, memoization, and tree-shaking.
  • The discussion also covered real-world challenges encountered during development.

• Part 2: Coding Task — Grid Light Box

The candidate was tasked with implementing a fully functional Grid Light Box component, suggesting a focus on UI development skills.

Round 3: Coding Task - Memoization

The candidate was required to implement a memoization wrapper for an asynchronous function. The provided function was:

async function getUserData(q, key, cb) {
 fetch(`localhost:3000/get/users?query=${q}&key=${key}`)
 .then(res => HTTPResponse(res, cb));
}

The goal was to create a memoizeUserData function that would cache results and prevent duplicate API calls for the same arguments, while also correctly handling asynchronous functions using callbacks. The follow-up questions tested the candidate's understanding of edge cases, such as:

• Handling JSON parameters in memoization.
• Addressing different key orders in JSON objects.
• Managing parallel API calls.

Round 4: Collaboration & Leadership