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Uber interview notes, page 56 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 interview with a binary matrix problem. Pretty clean question once you see the trick, but I fumbled around longer than I'd like to admit before getting there.
  • Given a binary matrix where every row is sorted in non-decreasing order, find the index of the leftmost column that contains at least one 1. Return -1 if no such column exists.

“My first instinct was just brute force every column left to right and scan down.”

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Uber·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Uber PM interview with a product design question focused on accessibility. Pretty open-ended, which I wasn't fully prepared for.
  • How would you design a product to help blind people navigate and shop at a mall?

“I spent the first minute going too broad, like listing every possible pain point a blind person might have anywhere ever.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026
Uber SWE coding round, one algorithmic question on subarrays. Pretty standard session, nothing too wild, but the problem had a subtle edge case that tripped me up a bit.
  • Given an array and an integer k, find the length of the shortest subarray that contains at least k distinct integer values. Return -1 if no such subarray exists.

“I knew sliding window was the right move pretty quickly, but fumbled a bit on when to shrink the window versus when to record the answer.”

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Uber·Software Engineer·Onsite - System Design / Architecture

Jul 2026
Uber system design round for a software engineer role. The question itself was straightforward but the interviewer spent a chunk of the session texting or replying to messages, which made the whole thing feel like a waste of time. Probably getting rejected from this one.
  • Design a daily puzzle platform where users submit answers and can view a leaderboard or score summary.

“The question itself wasn't bad.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026
Did a coding round with Uber that was pretty much one problem the whole time. The question looked straightforward at first glance but the optimal solution requires a non-obvious greedy insight on binary representations.
  • Given a positive integer n, you can add or subtract any power of 2 in a single operation. What is the minimum number of operations needed to reduce n to zero?

“My first instinct was to just count the set bits and call it a day.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026
Uber SWE coding round, got a grid problem that looked manageable at first glance but has enough edge cases to trip you up if you're not careful with the union-find or BFS approach.
  • Given an n x n binary matrix, you can flip at most one 0 to a 1. What is the maximum size of a 4-directionally connected island you can achieve after that flip?

“My first instinct was to BFS from every zero cell and count neighbors, which technically works but is way too slow on a 500x500 grid.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
Uber DS interview that went deep into coupon targeting and off-policy evaluation. The whole session was basically one giant case built around a $5 promo model, and they kept layering on sub-questions until I was pretty much drowning by part (d).
  • You have two coupon redemption models (a current one and a new one) each outputting a redemption probability per user. Define a decision policy that sends promos to the top users per day while keeping expected spend within a daily budget. What success metric would you use, and why is AUC or accuracy misleading here?
  • Using a historical randomized dataset with treatment assignments and outcomes, derive IPS, self-normalized IPS, and doubly robust estimators to compare the two model-induced policies. Write the formulas, state the assumptions needed for unbiasedness, and discuss variance trade-offs and cross-fitting.
  • How would you calibrate the model's predicted probabilities, set a daily targeting threshold to stay within budget under distribution shift, and directly optimize expected profit subject to guardrails like opt-out rate and complaint rate?
  • List three concrete data leakage risks in this coupon targeting setup and explain how you'd detect or prevent each one.
  • How do you handle delayed redemption labels and per-user redemption caps when training and evaluating the model, to avoid introducing bias?
  • Outline a monitoring plan for non-stationarity and cold-start users, including how you'd use shadow deployment and canarying in this context.

“I started with AUC out of habit and then had to walk it back, which was a bit embarrassing.”

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Uber·Machine Learning Engineer·Onsite - Multi Round

Jul 2026
Went through the staff-level MLE loop at Uber, which is only two rounds: one focused on org scope and influence, the other a deep architecture walkthrough. The bar is genuinely different from senior; they're not looking for incremental improvements, they want you to show you redesigned the problem itself. Feedback after a rejection is basically nonexistent, so debrief yourself immediately.
  • Walk me through a project you led recently in detail, including the architectural decisions and how the project was scoped.
  • How do you measure your scope of influence at a staff engineering level, and how many teams or engineers have you actually impacted?
  • What technical direction did you set on this project versus what direction were you given?
  • Present a complex system you've built, covering the major subsystems, latency budget, model choices, and how failures are handled.
  • What were the failure modes in this system and how did you handle on-call incidents related to it?
  • What alternatives did you consider and reject when designing this architecture?
  • What do you expect from a staff-level engineer on this team, specifically?

“This is the core of the first round and it runs longer than you'd expect.”

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Uber·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Uber PM interview, product sense round. One question about improving engagement on a streaming platform. Pretty open-ended, which I was not fully prepared for.
  • How would you improve user engagement on Netflix?

“I jumped straight into features without segmenting users first, which I think hurt me.”

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Uber·Frontend Engineer·Technical Phone Screen

Jul 2026
Coding screen for a frontend role at Uber. One question, LC 380. The interviewer was apparently on their bed during the call, which was a vibe I did not expect.
  • Implement a data structure that supports insert, delete, and get random element, all in average O(1) time.

“Classic combo of a hashmap and an array.”

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