Uber Interview Questions — Page 59

Uber·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Uber system design round for a software engineering role. One meaty question about a versioned key-value store that took up the whole session. The design constraints were trickier than they looked on the surface.

Design a time-versioned key-value store where writes do not accept a caller-supplied timestamp. It should support set(key, value), getLatest(key), and getAtOrBefore(key, wallTime). Walk through your data structures, how you guarantee monotonically increasing timestamps despite clock skew, and the time and space complexity of each operation.

“The no-external-timestamp constraint is what makes this annoying.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026

Uber DS interview with a meaty metric investigation case. The whole thing was one long question about a seasonal accident rate spike, and they clearly wanted you to go several layers deep, not just list hypotheses and call it done.

A monthly chart shows Uber's accident rate rising sharply from June through November, then falling quickly. The metric is reported accidents per 100,000 completed trips. Walk through how you'd investigate what's driving that pattern.

“This question is deceptively wide.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026

Uber SWE interview that included at least one coding problem. Nothing too surprising if you've done your prep.

Solve the Number of Islands II problem.

“Classic Union Find problem.”

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Uber·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Uber SWE system design round, one question the whole time. Pretty focused session around logistics and real-time data, which made sense given the domain.

Design a truck tracking system that supports filtering by truck number and provides an interface for updating driver status.

“I started with the data model and worked outward, which felt right but I probably spent too long on the database schema before touching the API layer.”

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Uber·Software Engineer·Technical Phone Screen

Jul 2026

Uber SWE interview with a tree path palindrome problem. Pretty niche stuff, the kind of question that sounds approachable until you realize the naive solution won't cut it at 10^5 nodes.

Given a tree where each edge is labeled with a lowercase character, count the number of unordered pairs of distinct nodes whose path edge-characters can be rearranged to form a palindrome.

“My first instinct was to just enumerate pairs and check each path, which obviously blows up.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026

Interviewed for a Data Scientist role at Uber and got a pretty heavy probability and numerical methods question that felt more like a software engineering problem than anything I'd prepped for. Not a vibe check round at all.

Given a continuous random variable with a known probability density function, how would you construct its cumulative distribution function? Cover both the case where the PDF is in closed form and where you only have sampled (x, f(x)) pairs. Also address infinite support, piecewise PDFs, normalization, and how to ensure the result is actually non-decreasing and bounded between 0 and 1.

“This took me a second to parse because it sounds like a stats textbook question but they really wanted code or a concrete algorithm.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Uber ML engineer screen, just one coding question the whole time. Pretty straightforward problem on paper but I second-guessed myself more than I should have.

Given an integer array, find the index of the first local minimum. A local minimum is an element strictly smaller than its immediate neighbors, with edge cases handled for the first and last positions. Return -1 if none exists.

“I jumped straight to binary search because I'd seen similar problems before, then realized mid-explanation that the problem asks for the FIRST local minimum by index, not just any one.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026

Uber data scientist interview that went deep into experimentation design. Four-part question on A/B testing that covered everything from sample size math to causal inference with DAGs. More technical than I expected for a single session.

Given a signup funnel A/B test with a baseline conversion of 5%, a target relative lift of 10%, daily traffic of 50k with 60% targeting match and 90% bucketing success, a 1:1 split, alpha 0.05, and 80% power: compute the required sample size per variant and the expected calendar duration to reach it. State your formula and variance assumptions.
For a proportion-based primary metric in an A/B test, would you use a Z-test or a T-test, and why? When do their results meaningfully diverge? Also address how estimating variance from data, small-sample corrections, and unequal group sizes affect the choice.
If a product stakeholder wants to stop the test early whenever results look promising, how would you design a sequential testing or alpha-spending framework to control Type I error? Describe the stopping rules and how they affect nominal sample size and expected test duration.
If randomization isn't feasible for a related rollout, sketch a causal inference plan: draw a DAG to identify confounders, propose an identification strategy such as difference-in-differences or instrumental variables, and list the key assumptions you'd need to defend and how you'd validate them.

“I knew the pooled proportion formula but fumbled the traffic adjustment.”

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Uber·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Uber MLE interview with a dynamic programming problem on counting ways to reach a target score. Pretty standard bounded knapsack territory but still worth thinking through carefully before diving into code.

Given a list of question types where each type has a fixed number of available questions and a fixed point value per question, count the number of distinct ways to score exactly a target number of points. Return the result modulo 10^9 + 7.

“This is basically a bounded knapsack / DP counting problem.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026

Uber data scientist interview, stats-heavy technical screen. The whole thing was basically one long hypothesis testing question with a few layers stacked on top of each other. Not a vibe-check round at all.

For an email marketing A/B test comparing click-through rates between a control and a test subject line, how would you set up the null and alternative hypotheses, justify using a z-test via the Central Limit Theorem, and derive the sample size needed to detect a 2 percentage point lift with 80% power at a 0.05 significance level?

“This is three questions dressed up as one and I didn't realize that until I was halfway through answering the first part.”

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