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Uber interview notes, page 6 of 61

Large Enterprises

Most of these Uber write-ups come from Software Engineers (407), then Data Scientists (78) and Machine Learning Engineers (65). The most reported rounds are the technical phone screen (387), the system design round (95), the product sense round (46) and the onsite coding round (23).

609 interview notes · updated Aug 2026

Uber·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Uber product interview, one question about merging Uber Eats into the main app. Short and a bit sparse as far as interview experiences go.
  • How would you integrate Uber Eats into the core Uber app?

“This is a meaty one if you let it be.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Uber SWE interview with a pipeline throughput optimization problem. The core challenge was figuring out the right algorithmic approach under pressure, and binary search on the answer wasn't the first thing that came to mind.
  • You have a series of services running in sequence where the overall throughput is bottlenecked by the slowest service. Each service has a base throughput and can be scaled up at a cost proportional to the scale factor. Given a fixed budget, how do you maximize the overall pipeline throughput?

“Took me a while to see the binary search angle.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Uber coding screen for a software engineer role, pretty standard array manipulation problem but the follow-up is where things got interesting.
  • Given a sorted array of integers, return a new array of each element squared, also in sorted order. Then optimize it to run in O(n) time.

“The naive approach is obvious, square everything and sort it, but that's O(n log n) and they pushed for better.”

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Uber·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Uber onsite for an SWE role, ML system design round. The whole thing was a deep dive into end-to-end ML systems, recommendation pipelines, and experimentation design. Pretty intense if you haven't thought carefully about train-serve skew or two-stage retrieval before.
  • Design a restaurant recommendation system for Uber Eats end-to-end, from feature pipeline to serving to evaluation.
  • How would you handle cold-start for new restaurants or new users in your recommendation system?
  • How do you ensure that the features used during training match the features available at serving time?
  • Walk me through how you'd design the evaluation framework for this system, both offline and online.
  • How would you design the feature pipeline to support both batch training and low-latency online serving?
  • How would you design an ETA prediction system, and what metrics would you use to evaluate it?
  • How would you monitor a deployed recommendation model for drift, and how often would you retrain?

“This is the big one.”

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Uber·Data Scientist·Onsite - Behavioral / Leadership

Apr 2026
Second behavioral and case-study round at Uber for a Data Scientist role, basically a repeat format of an earlier project deep dive but with higher expectations around breadth and depth.
  • Walk me through a project you haven't discussed in previous rounds, focusing on the design decisions and trade-offs you made.
  • Go deeper on a specific sub-system or component from a project you've already discussed. What did you only briefly mention before, and why did you make the choices you did there?
  • How would your design change if the scale increased by 10x? What breaks first and what would you do about it?
  • If you were optimizing for a completely different success metric than the one you used, how would that change your approach?
  • How did you handle a situation where key data was missing or unavailable? What assumptions did you make and how did you validate them?

“The pressure to pick something genuinely different from what I'd already covered tripped me up a bit.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Uber SWE coding round, one algorithmic question the whole time. Pretty standard backtracking problem but the edge case discussion went longer than I expected.
  • Given a string of digits (each between 2 and 9), return all possible letter combinations those digits could produce using a standard phone keypad.

“I went straight to backtracking and it felt fine.”

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Uber·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Onsite coding round at Uber for a SWE role. One question, seat assignment, which sounds straightforward until you're actually implementing it under pressure.
  • Implement a class that assigns seats one at a time in a row of n seats, always picking the seat that maximizes the minimum distance to any already-occupied seat. The first person goes to seat 0, and seats are never vacated.

“I recognized this as basically LeetCode 855 but without the leave() part, which actually made it simpler.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Uber SWE interview with a stream-processing design problem. Pretty focused session, just the one question but they dug into it pretty thoroughly.
  • You're given a stream of customer visit events (just customer IDs in arrival order). Design a data structure with two operations: record a visit for a given customer ID, and return the most recently arrived customer who has been seen exactly once so far. Return null if no such customer exists.

“I started with just a hashmap for counts which was fine, but I fumbled on the 'most recent single-hit' part for a bit.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Uber SWE coding round, just one question about Minesweeper. Pretty straightforward on the surface but the 2D array printing part tripped me up a bit.
  • Implement a Minesweeper board: randomly place a set of mines on a 2D grid, then print the full grid.

“Spent too long overthinking the random placement logic and not enough time on clean output formatting.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Phone screen at Uber for a SWE role, one algorithmic question on service dependency ordering. The interviewer pushed hard on complexity analysis and apparently that's where a lot of candidates slip up.
  • Given a service dependency graph and a target service, return a valid build order that includes the target and all of its transitive dependencies. Follow-up: what if the graph contains cycles?

“The core problem is basically course schedule, so I had seen it before.”

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