Uber Interview Questions — Page 38

Uber·Software Engineer·Technical Phone Screen

Jun 2026

Interviewed for a software engineer role at Uber, two algorithm questions back to back. Both were pretty meaty and required thinking through binary search and dynamic programming under time pressure. Felt okay about the first one, shakier on the second.

You have an array of vault sizes and a time limit in hours. A robber can steal up to k units per hour from one vault at a time, and a vault of size v takes ceil(v/k) hours to empty. Find the minimum integer rate k such that all vaults can be emptied within the given number of hours.
Given a set of integers and a target value, determine whether any subset of the integers sums exactly to the target. Return true or false.

“Classic binary search on the answer, which I did recognize pretty quickly.”

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Uber·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Uber system design round for a software engineering role, centered entirely on a meeting room booking system. The interviewer pushed back mid-implementation and asked for a redesign, which I was not fully prepared for.

Design an object-oriented Meeting Room Booking System with support for multiple rooms, overlapping booking rejection, recurring bookings, time-zone handling, and conflict resolution. Produce a class diagram and implement the core booking and availability logic in code.

“I started with the obvious classes: Room, Booking, User, BookingService.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026

Interviewed for a SWE role at Uber and got the classic knight moves BFS problem. Pretty standard algorithmic round, nothing too wild.

Given an infinite chessboard and a knight starting at [0, 0], find the minimum number of moves required to reach a target square [x, y].

“BFS was the obvious approach and I went with it.”

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Uber·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round at Uber for a data analyst role. Just one question but it took up most of the conversation, which I wasn't expecting.

Describe a situation where the business problem you were given wasn't clearly defined. What did you do?

“I fumbled the opener a bit because I picked an example that was too clean in hindsight.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Uber ML engineer screen, one coding problem that looks easy until you realize the naive approach won't cut it on time complexity. Pretty standard vibe but the follow-up on optimization is where it gets real.

Given a sorted array of integers, return a new array of the squares of each element, also sorted in non-decreasing order. Can you do it in O(n) time?

“My first instinct was just square everything and sort, which is O(n log n) and they immediately asked if I could do better.”

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Uber·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round for a Data Scientist role at Uber. The whole thing was basically one long, layered case question about owning a messy analytics project from start to finish. Pretty intense for what I expected to be a softer round.

Walk me through an analytics project you led end-to-end where the goals were unclear and you were working under a tight deadline. What did success look like in measurable terms, and how did you scope and prioritize when different stakeholders wanted different things?
Describe a time you pushed back on a senior stakeholder who wanted to use a metric you thought was misleading. What evidence did you bring, and how did you handle the conversation?
What concrete impact did the project have, and how did you make sure the work was reproducible and transferable to whoever came after you?
Looking back at that project, what's one decision you'd change and what early signal would have told you sooner that you needed to course-correct?
If you had to redo this project as a two-week sprint starting today, what would your day-by-day milestone plan look like, and how would you track whether you're on the right path before the final results are in?

“This is where I spent most of the interview.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026

Uber SWE interview with a graph traversal problem. The question looked like a math problem at first glance but it's really just weighted graph search once you see it.

Given a set of division equations like A / B = k, and a list of queries asking for the value of C / D, return the computed result for each query or -1.0 if it can't be determined from the given equations.

“Took me an embarrassingly long time to stop thinking about it as algebra and realize it's just a weighted directed graph where each equation is an edge.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026

Uber SWE coding round, got a binary search problem dressed up as a key-value store design. Pretty standard once you see what they're actually asking for.

Design a time-based key-value store that supports storing multiple values per key at different timestamps, with a lookup that returns the value at the largest timestamp less than or equal to the queried time.

“Took me a minute to stop thinking about this as a system design thing and realize it's just binary search on a sorted list of timestamps.”

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Uber·Machine Learning Engineer·Onsite - Product Sense / Strategy

Jun 2026

Uber MLE onsite with a meaty business case around delivery unit economics. The whole thing was basically one long case question broken into three parts, and the experimentation section at the end is where things got hairy for me.

Uber Eats occasionally gets long-distance delivery orders that are barely profitable or outright money-losers. How would you frame and break down what's driving this problem?
What solutions would you propose to fix the unprofitability of long-distance orders on a platform like Uber Eats?
Design an experiment to validate your proposed solution. Walk through your hypothesis, metrics, randomization strategy, and how you'd handle network effects and novelty effects.

“I started by separating supply-side from demand-side costs, which felt right.”

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Uber·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026

Uber MLE system design round, pretty deep dive into recommendation systems. The scope was broad enough that I kept second-guessing whether to go deeper on one area or keep moving. Left feeling like I covered the surface of everything and the core of nothing.

Design an end-to-end restaurant recommendation system, covering personalization on the home feed, search, and a 'near me' feature.
How would you design the candidate generation stage, and what retrieval strategies would you use?
Walk me through your ranking model design, including what features you'd use and whether you'd go pointwise or pairwise.
How would you design the feature store, and how do you handle freshness and consistency between online and offline environments?
What does your serving architecture look like, and how do you hit sub-200ms p99 latency?
How would you set up the feedback loop and define the signals you'd use to improve the model over time?
How would you evaluate the system both offline and online?

“This is one of those questions where you think you know where to start and then realize 20 minutes in you've been talking about geo filtering for way too long.”

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