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Uber interview notes, page 8 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

Apr 2026
Uber SWE interview with a tricky optimization problem that looks like brute force bait but has a clean ternary search solution once you see the unimodal structure. Nothing behavioral, just the one algorithmic problem.
  • You're climbing N floors of a building. You take an elevator for the first x floors (each floor costs t1 time) and stairs for the remaining N-x floors (each stair floor costs e2 energy and takes ceil(c / current_energy) time, with a fixed energy budget E that can't go negative). Find the value of x that minimizes the maximum of the two phase times.

“My first instinct was to just iterate over all x and pick the minimum, which works but the O(N^2) felt gross and I said so out loud which I think was fine.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Phone screen for a SWE role at Uber. Warm-up coding question but the follow-up discussion went deeper than I expected, so don't go in thinking it's a throwaway.
  • Given an array of daily stock prices, find the maximum profit you can make by buying on one day and selling on a later day. Return 0 if no profitable trade is possible.

“Knew this one cold so I jumped straight to the linear scan approach, tracking the running minimum and updating max profit as I go.”

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Uber·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Uber system design interview with a behavioral twist, basically asked me to walk through a real architectural decision I'd made and defend the reasoning. One question, but it had a lot of layers.
  • Describe a situation where you changed a system design due to an important trade-off. Walk through the original design, the competing constraints, the alternatives you considered, your reasoning for switching direction, and what happened as a result.

“The part that tripped me up was they wanted all five pieces: original design, competing goals, options considered, why I pivoted, and the outcome.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Uber SWE interview with a tree traversal problem that looked like a standard BFS/DFS warmup until it wasn't. The outer boundary framing added enough wrinkle to keep me second-guessing my approach the whole time.
  • Given the root of an N-ary tree, compute the 'outer boundary path': starting from the deepest leftmost node, walk up to the root following leftmost nodes at each depth, then walk back down following rightmost nodes at each depth (skipping any depth where the leftmost and rightmost node are the same). Return the sequence of node values in that order.

“My first instinct was to just do a BFS and track the first and last node at each level, which is roughly right, but I kept fumbling the dedup condition.”

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Uber·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PM strategy question for Uber, framed around Google News. One question, open-ended, the kind where you can talk forever and still feel like you said nothing useful.
  • You're the PM for Google News. Walk me through your product strategy for the next year.

“I spent the first minute trying to figure out if they wanted me to be aspirational or grounded and I picked wrong.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Uber SWE interview with a bitmask DP problem on hat assignments. Pretty niche problem type and I hadn't seen it before, so it was a rough ride.
  • Given n people (up to 10) and 40 hat types, where each person specifies which hats they're willing to wear, count the number of ways to assign exactly one hat per person such that no two people share a hat. Return the result modulo 10^9 + 7.

“I knew it was some kind of DP but spent way too long thinking about it person-by-person before realizing you should iterate over hats instead, since there are only 40 hats but up to 10 people.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Uber SWE interview with a tree manipulation problem. Pretty standard algorithmic round but the O(1) space constraint is what makes it actually interesting.
  • Given a perfect binary tree where every leaf is at the same depth, populate each node's 'next' pointer to point to its next right neighbor on the same level, or NULL if none exists. Try to do it with O(1) extra space.

“The naive BFS solution comes to mind immediately but they pushed back on the space usage right away.”

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Uber·Data Scientist·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round for a Data Scientist role at Uber. Three questions, all pretty deep, and the project deep-dive in particular felt like it could go anywhere depending on what you'd worked on. Left feeling okay but not great.
  • Tell me about a time you went above and beyond what was expected of you. What was the original scope, what did you do on your own initiative, and what was the measurable result?
  • Describe a time you disagreed with your team or stakeholders, but the project still failed in the end. How did you raise your concerns, and what would you do differently?
  • Walk me through a project you worked on in depth, including the problem, your specific contribution, the technical approach, the tradeoffs you navigated, and the final impact. Be ready for detailed follow-ups.

“I had a decent story here but I fumbled the measurable impact part.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026
Uber SWE interview with a tree construction problem that looks straightforward until you're actually in it. The recursive structure is clean in theory but I kept second-guessing my base cases.
  • Given an n x n binary matrix, build a Quad-Tree representation where each node stores whether it's a leaf and its value, recursively subdividing regions that aren't uniform.

“I got the general idea pretty fast, uniform region becomes a leaf, otherwise split into four quadrants and recurse.”

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Uber·Data Scientist·Technical Phone Screen

Apr 2026
Uber DS interview focused entirely on a surge pricing notification system for drivers. The whole thing was one big open-ended case with several layers peeled back one at a time, which I wasn't fully prepared for.
  • What are the business pros and cons of sending surge-pricing push notifications to nearby drivers?
  • Design a ranking system that decides how many drivers to notify and which specific drivers to target for surge alerts.
  • The current approach of using a fixed radius to determine which drivers to notify is inadequate. Why, and how would you improve it with data-driven methods?
  • What would you use as a proxy for driver ETA, and what metrics would you define and compute around it?
  • What additional real-time or historical signals should influence which drivers receive a surge push notification?
  • If neighborhood-level supply-demand imbalance is used as a feature, how would you detect and quantify that imbalance?

“Started okay.”

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