Uber Interview Questions — Page 10

Uber·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

Uber product sense interview where they asked me to dig into an underperforming feature. Pretty open-ended, which I wasn't fully prepared for.

Which Uber feature has potential but hasn't lived up to it yet? Walk through your analysis of the product, opportunities you see, expected impact, how you'd launch it, and how you'd measure success.

“I froze for a second picking the feature.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026

Uber SWE coding round, pretty standard graph/union-find territory but the follow-up on deletion is where things got interesting and honestly a bit uncomfortable.

You have an m x n grid of water. Land cells are added one at a time. After each addition, return the number of connected islands (4-directional adjacency).
Follow-up: how would you modify your solution to also support deleting land cells, not just adding them?

“Went with union-find pretty quickly, which felt right.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026

Uber SWE interview with a graph-based division problem. The question looked like a math puzzle at first but it's really a graph traversal under the hood.

You're given a list of equations like A/B = k (where k is a real number) and a list of queries asking you to compute C/D. Return the result for each query, or -1.0 if it can't be determined.

“Took me an embarrassingly long time to see this as a graph problem.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026

Uber coding round, algorithm-heavy. One problem but it had two parts and the monotonic stack angle wasn't immediately obvious to me.

Given an array of item prices and a discount rule where each item gets a discount equal to the price of the next cheaper-or-equal item that appears after it in the array, compute the total amount paid after discounts and identify which items received no discount at all. Solve it in O(n).

“Two-parter which I didn't see coming.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026

Uber SWE interview with a graph problem that looks deceptively simple on the surface. The core challenge was knowing the rerooting DP pattern, which I'd seen before but never had to implement cleanly under pressure.

You're given a directed graph on n nodes (labeled 0 to n-1) whose underlying undirected structure is a tree. For each node, find the minimum number of edge reversals required so that every other node is reachable from that node.

“I knew this was a tree DP problem pretty fast, but the rerooting part is where I fumbled.”

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Uber·Software Engineer·Technical Phone Screen

Apr 2026

Uber software engineer interview with a friendly Chinese interviewer who kicked things off in Mandarin before switching to English. Got the Tiny URL system design question, which felt like a bit of a gift compared to what I'd been prepping for.

Design a URL shortening service (similar to TinyURL).

“Interviewer picked this one and I could tell it was the easier path.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Interviewed for an ML engineer role at Uber and got hit with a computer vision fundamentals question. Pretty short session from what I remember, nothing too crazy but it did make me realize how rusty I was on the theory side.

Can you explain what semantic segmentation is and how it works?

“I knew the gist of it, assigning a class label to every pixel in an image rather than just drawing a bounding box around objects.”

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Uber·Data Scientist·Technical Phone Screen

Apr 2026

Uber DS interview that went deep into marketplace operations. The main question was a multi-part monster covering metric design, optimization modeling, and experiment design all in one sitting. Walked out not totally sure how I did.

How would you define and measure driver backtracking in a ride-share marketplace? Specifically, design a quantitative metric per driver-hour using GPS and assignment logs, and describe how you'd detect and validate backtracking segments.
Formulate an optimization model that assigns drivers to ride requests and repositioning tasks to minimize expected backtracking, while satisfying constraints on ETA service levels, minimum driver utilization, zone fairness, and a cap on repositioning costs. State decision variables, objective function, and constraints explicitly.
Which modeling approach would you choose for this problem, such as time-expanded network min-cost flow or a mixed-integer program, and how would you solve it in real time? Discuss options like rolling horizon, column generation, or Lagrangian relaxation and comment on approximation quality.
How would you evaluate improvements to the backtracking reduction model both offline and through an online experiment?
How would you handle demand uncertainty in this optimization model? Walk through using robust or stochastic optimization, and illustrate with a small example using three zones and five-minute intervals.

“I started with a basic 'ratio of distance traveled backward vs forward' framing and the interviewer pushed back pretty fast.”

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Uber·Software Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at Uber for a software engineer role. The whole session was basically one big question about building a location-aware food delivery platform, and it went deep fast.

Design a simplified food delivery platform where merchants can register restaurants with location and menu data, and customers can query for the nearest restaurants within a given radius, sorted by distance. The system needs to support 100M+ daily active users globally with sub-100ms read latency and strong consistency on writes.

“This one sprawled in every direction.”

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Uber·Data Scientist·Technical Phone Screen

Apr 2026

Uber DS interview focused almost entirely on CTR prediction for ads, and it went deep fast. Five interconnected questions covering modeling, imbalance handling, evaluation metrics, calibration, and production validation. The kind of loop where you realize halfway through that they actually want you to know this stuff cold.

You're building a model to predict whether an ad impression leads to a click within 24 hours. The positive rate is around 0.7%. Propose two model families suited for extreme class imbalance and high-cardinality sparse features. How would you encode ad_id and campaign_id without leaking information, and what does your cross-validation scheme look like given that some click labels arrive up to 24 hours late?
Compare class weighting, focal loss, undersampling, and calibrated thresholding for handling severe class imbalance. In what situation would you avoid synthetic oversampling, and how do each of these choices affect ranking performance versus calibration?
Model A has ROC-AUC of 0.91 and PR-AUC of 0.14. Model B has ROC-AUC of 0.88 and PR-AUC of 0.22. Why can these metrics disagree at 0.7% prevalence, which model would you trust for ad CTR prediction, and how do you use a cost matrix around missed clicks versus wasted impressions to pick an operating threshold?
How would you assess and fix model calibration using isotonic regression versus Platt scaling? Walk through selecting a threshold to maximize F1 versus one that maximizes expected profit. How do you compute precision at the top 1% of scores and use it to compare models?
Design a bucket test to validate whether the model's scores actually lift CTR in production using a top-k targeting setup. What logs do you need to detect covariate drift and label delay, and how do you prevent feedback loops from corrupting future training data?

“This was the opener and it set the tone.”

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