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Uber interview notes, page 53 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·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Uber SWE interview with a system design question that had a product twist to it. Pretty focused session, just the one problem but they pushed on the details.
  • Design an Amazon-style e-commerce product page that also tracks the number of times the page has been viewed.

“The view tracking part is where I spent most of my energy and probably where I should have been more careful about tradeoffs.”

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Uber·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026
Interviewed for a BizOps role at Uber and got a classic market expansion case. Pretty standard for this type of role but the question had enough layers to trip you up if you're not careful.
  • As part of the BizOps team at Uber, what factors would you consider when deciding which cities to launch ridesharing in for an emerging market?

“I went straight for the obvious stuff first: population density, smartphone penetration, existing transport infrastructure.”

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Uber·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM strategy round at Uber, basically one big open-ended question about market expansion. Not a lot of structure given upfront, which made it feel more like a conversation than a case, for better or worse.
  • You're a PM at Uber. Which market should Uber expand into next, and why?

“I went straight into frameworks and probably over-indexed on TAM sizing before actually anchoring on what Uber's core strengths are.”

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

Jun 2026
ML system design round at Uber for an MLE role. The whole session was basically one big fraud detection question that kept branching into sub-topics. Walked away feeling okay about it but not confident I covered everything they wanted.
  • Design an end-to-end machine learning system for fraud detection on payments or accounts.
  • How would you choose between decision trees, gradient-boosted trees, and neural networks for a fraud detection model, and what are the tradeoffs?
  • How do you properly evaluate a fraud model given that fraud events are bursty and labels arrive with a delay?
  • What metrics would you use to measure the performance of a fraud detection system, and why?
  • How would you scale this fraud detection system when the data volume exceeds what a single machine can handle, and how do you keep inference latency low?
  • How does the model connect to real-time user-facing actions like blocking a transaction or routing to manual review, and how do those outcomes feed back into training?

“This was the whole interview basically.”

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Uber·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Second round at Uber for a software engineering role. One coding question plus a follow-up extension, then a tradeoff discussion to close it out.
  • Given a binary tree where each node holds a digit, compute the sum of all numbers formed by root-to-leaf paths.

“Felt pretty comfortable with the base case but the follow-up threw me off.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026
Uber coding round for a software engineer position. One graph problem the whole time, but it had enough nuance to keep me on my toes for a while.
  • Given a directed grid or graph, find the minimum number of edge reversals needed to travel from a source node to a target node. Traversing an edge in its existing direction costs nothing; going against it costs 1 reversal.

“I knew BFS but initially reached for Dijkstra out of habit.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026
Uber SWE interview with a scheduling/interval problem that looked deceptively simple but had a real efficiency angle baked in. The kind of question where getting a working solution isn't enough, they want you to think about what sorted order actually buys you.
  • You have multiple meeting rooms, each with a sorted list of non-overlapping booked intervals. Given a new meeting interval, find any room where it can be inserted without causing a conflict, or return -1 if none exist. How do you do this efficiently?

“My first instinct was to just loop through every room and every interval inside it, which works but is obviously brute force.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026
Uber SWE interview with a sliding window problem that looks straightforward but has a few gotchas once you start counting pairs correctly.
  • Given an integer array and a value k, count how many contiguous subarrays contain at least k pairs of equal elements, where each distinct value contributes at most one pair no matter how many times it appears.

“My first instinct was two pointers and I think that's right, but I fumbled the pair-counting logic initially.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026
Uber SWE phone screen that was basically just LC 815 verbatim. The problem itself isn't hard once you know the route-as-node trick, but if you've never seen it before you're probably building a stop-level BFS and wondering why it's slow.
  • Given a list of bus routes (each route loops through its stops forever), find the minimum number of buses you need to ride to get from a source stop to a target stop. Return -1 if it's unreachable.

“The key thing nobody tells you upfront: BFS over routes, not stops.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Uber ML engineer interview with a pretty deep numerical methods question. Not what I expected from a typical coding screen, felt more like a technical phone screen with a strong algorithms slant.
  • Implement a Python algorithm to minimize a 1D convex black-box function over a closed interval, without access to gradients. Cover method choice, stopping criteria, complexity, edge cases, and provide working code with a testing plan.

“This one hit me sideways.”

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