Uber interview notes, page 39 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·Onsite - System Design / Architecture

Jun 2026

Uber system design round for a software engineer role. The whole session was basically one big deep-dive into a stock price alert system, covering everything from requirements to delivery guarantees. A lot of ground to cover in one interview.

Design a stock price alert system where users can set threshold-based alerts on securities and receive notifications when the market price crosses that threshold. Walk through requirements, APIs, data model, architecture, alert evaluation strategy, scalability, latency, reliability, deduplication, and notification delivery across push, email, and SMS.

“This is a lot to unpack in one question.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026

Went through a coding round at Uber for a software engineer role. Pretty standard algorithmic problem but there's more nuance to it than you'd expect if you haven't thought about palindromes in a while.

Given a string, find and return the longest palindromic substring.

“My first instinct was the DP table approach because it felt safe and I could explain it cleanly.”

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Uber·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026

Uber MLE system design round, full session on building a restaurant ranking system for a food delivery product. The question had a lot of moving parts and I don't think I covered everything as cleanly as I wanted to.

Design a personalized restaurant recommendation and ranking system for a food delivery platform like Uber Eats. The system should rank restaurants on the home feed based on user history, cuisine preferences, time of day, location, delivery ETA, fees, and live marketplace conditions.
How would you define success metrics for this recommendation system, and what guardrail metrics would you put in place?
Walk through your candidate generation strategy. What retrieval approaches would you use and how would you merge candidates from multiple sources?
What ranking model would you choose and what targets would you train it to predict? How do you handle multiple objectives like clicks, orders, and long-term retention?
How would you handle position bias in training data, and what steps would you take to prevent feedback loops in the ranking system?
How would you approach cold start for both new users and new restaurants?
Describe the online serving architecture for this system. How do you meet latency requirements while keeping features fresh?
How would you design the experimentation and monitoring setup for this system, and what failure modes would you watch for?

“This was the whole interview basically.”

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Uber·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Uber product interview, one question about the UberX marketplace. Not much context given so hard to say how it went, but the question itself was meaty enough to chew on for a while.

How would you describe and evaluate the performance of the Uber Marketplace for UberX?

“This one has a lot of surface area.”

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Uber·Data Scientist·Technical Phone Screen

Jun 2026

Uber DS interview that went deep into causal inference territory. The question was essentially one big design problem about estimating a promotion's effect without being able to run an experiment, and they wanted you to walk through multiple strategies in real time. Technically dense and a bit exhausting.

You can't run an experiment to measure a promotion's causal effect. Walk through at least three identification strategies you'd use, including the assumptions each relies on, how you'd check those assumptions, and where each approach could break down.

“This is a beast of a question.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026

Uber SWE interview with a classic BFS lock problem. Nothing too crazy but it required knowing your graph traversal cold, no hand-holding.

You have a 4-wheel combination lock where each wheel has digits 0-9 and wraps around. Starting from '0000', given a list of forbidden codes and a target code, find the minimum number of single-wheel turns to reach the target, or return -1 if it's not possible.

“Took me a minute to see it as a graph problem.”

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Uber·Machine Learning Engineer·Onsite - Multi Round

Jun 2026

Uber ML Engineer interview that leaned hard into system design and behavioral depth at the same time. Two meaty questions, both asking you to go end-to-end on real projects rather than toy examples. Left feeling like I either crushed it or completely missed what they were looking for.

Walk me through a project where you built an AI function calling system from scratch. Cover the problem, your role, the key technical decisions around APIs and data modeling, the challenges you hit, and what the actual impact was.
Tell me about a time you hit a serious blocker or conflict while driving a project, like a cross-team disagreement or a priority clash. How did you figure out what was actually wrong, get people aligned, and make the right trade-offs to keep moving?

“This one is deceptively open-ended.”

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Uber·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Round 4 at Uber for a software engineering role was a deep dive where you present a project via slides and field behavioral questions. Budget about 25 minutes to walk through your project and leave room for questions at the end.

Walk us through a project you worked on in depth, using slides you prepared in advance.

“25 minutes is longer than it sounds until you're actually in it.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026

Uber SWE interview with a sliding window problem that looks straightforward until you actually have to implement it under pressure.

Given an array of integers and a limit value, find the length of the longest subarray where the absolute difference between any two elements stays within that limit.

“The sliding window part clicked pretty fast.”

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Uber·Software Engineer·Technical Phone Screen

Jun 2026

Uber coding round, array manipulation problem with a discount rule baked in. Pretty standard stuff but the follow-up about efficiency tripped me up a bit.

Given an array of prices, apply a discount to each item where the discount equals the first price to its right that is less than or equal to it. If no such price exists, keep the original price. Return the resulting array.

“Started with the brute force loop-within-a-loop approach and it worked fine.”

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