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Uber interview notes, page 57 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·Technical Phone Screen

Jul 2026
Uber SWE coding round, got a stack-based simulation problem about tracking CPU function execution time. Pretty standard algorithmic question but the edge cases tripped me up a bit.
  • Given execution logs for n functions running on a single-threaded CPU, compute the exclusive time each function spends executing, not counting time spent in any nested calls.

“My first instinct was to just iterate through logs and subtract child durations, but that falls apart fast with deeply nested calls.”

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Uber·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Uber for a software engineering role. The prompt was designing the shopping cart for Uber Eats, which sounds contained until you actually start pulling on the threads.
  • Design the shopping cart system for Uber Eats. Walk through the architecture end to end.

“Spent probably too long on the data model upfront and had to rush the concurrency stuff later, which is honestly where the interesting problems live.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026
Uber SWE coding round, one tree problem the whole session. Pretty standard stuff if you've done any LeetCode prep, but the pressure of doing it live is a different thing entirely.
  • Given the root of a binary tree and two nodes p and q, find their lowest common ancestor.

“I knew this problem but still fumbled the explanation for like two minutes.”

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Uber·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Uber PM interview with a product sense question about ride cancellations. Pretty standard case but there's more depth to it than you'd expect if you go in underprepared.
  • You're the PM for a ride-sharing app. How would you improve the customer experience and bring down ride cancellation rates?

“I started with the user segments affected and tried to separate driver-side cancellations from rider-side ones because the root causes are completely different.”

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Uber·Machine Learning Engineer·Onsite - Multi Round

Jul 2026
Uber ML Engineer interview that covered two pretty meaty areas: a deep dive into a system I'd actually built end to end, and a behavioral question about handling disagreements with teammates. Both felt like they wanted real stories, not textbook answers.
  • Walk me through an ML system you designed or owned from scratch. Cover the problem, business goal, data, features, model selection, training, evaluation, deployment, monitoring, and what impact it had.
  • How do you handle disagreements with teammates or cross-functional partners, especially around model choice, metrics, or product direction? Give a specific example.

“This is the kind of question that sounds easy until you're actually in it and realize you're trying to compress 18 months of work into 15 minutes without losing the thread.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
Uber data science interview focused entirely on A/B testing around a promotional campaign. Five questions, all connected, building on each other in a way that felt more like a case study than a standard interview. Pretty intense if you haven't thought carefully about experiment design end to end.
  • Why would a company want to run a promotional campaign like this, and if it's limited to one city or market, what's the reasoning behind that restriction?
  • What metrics would you use to evaluate whether the promotion worked? Walk through primary, secondary, and guardrail metrics.
  • How do you figure out the right sample size for an experiment like this? What factors and inputs go into that calculation?
  • The PM sets the minimum detectable effect on revenue at 0.5%. What does that actually mean, and how does it change your sample size requirements?
  • The experiment shows a +0.3% lift in revenue but it's not statistically significant. How do you interpret that and explain it to the PM?

“I jumped straight to revenue uplift and forgot to talk about the geo-restriction angle until they nudged me.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026
Uber SWE interview with a classic stock profit problem. Nothing too wild, pretty standard coding round stuff.
  • Given an array of daily stock prices, find the maximum profit from a single buy-sell transaction. If no profit is possible, return 0.

“Pretty much a sliding window or single-pass min-tracking problem.”

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Uber·Software Engineer·Online Assessment (OA)

Jul 2026
Uber SWE online assessment, 90 minutes, two algorithmic problems. Both were harder than I expected for an OA format, and the tree one in particular took me a while to even understand what was being asked.
  • Given a permutation of integers 1 through N, for each prefix of values {1, 2, ..., k}, determine whether those values occupy a contiguous segment in the array. Return a binary string of length N encoding the answer for each k.
  • You are given a directed graph on N nodes with N-1 edges that forms an undirected tree. Find the minimum number of edge reversals needed so that all edges point away from some chosen root, minimized over all possible root choices.

“I spent way too long on edge cases before I even had a working solution.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026
Uber SWE interview with a monotonic stack problem that looks straightforward but has a sneaky second part most people probably miss on first read.
  • Given a prices array, apply a discount rule where each item's discount equals the price of the next cheaper-or-equal item that comes after it. Return both the sum of all final prices and the list of indices where no discount was applied, using O(n) time.

“The monotonic stack part clicked pretty fast for me, standard next-smaller-element pattern.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Uber MLE interview with a graph/BFS problem that looks deceptively clean on the surface. The bus routes question took me longer than I expected to untangle.
  • You're given a list of bus routes, where each route is a circular sequence of stops a bus repeats indefinitely. Starting at a given stop, find the minimum number of buses you need to board to reach a target stop. Return -1 if it's unreachable.

“My first instinct was Dijkstra and that was the wrong direction to go down.”

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