← Back to Directory

Uber interview notes, page 29 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·Product Manager·Onsite - Product Sense / Strategy

May 2026
Uber PM interview with a product sense question about airport passenger experience. Not much context to go on, but it's the kind of open-ended design question that can go a lot of different directions depending on how you scope it.
  • How would you improve the passenger experience at a major metropolitan airport?

“This one is deceptively wide.”

View Post

Uber·Software Engineer·Technical Phone Screen

May 2026
Uber phone screen for a SWE role, one main coding problem about simulating a single-elimination tennis tournament, plus a harder follow-up that reversed the whole thing. Pretty approachable as phone screens go, but the follow-up had some real teeth if you weren't careful about odd-sized brackets.
  • Given an array of player rankings (length is a power of 2), simulate a single-elimination tournament where adjacent pairs compete each round and the higher-ranked player advances. Print the list of survivors after every round.
  • Given n, generate a seeding arrangement of ranks 1 through n such that in a single-elimination bracket, weaker players (higher rank numbers) get eliminated in earlier rounds. Solve it for any n, not just powers of 2.

“Straightforward simulation once you see it.”

View Post

Uber·Software Engineer·Technical Phone Screen

May 2026
Three coding problems back to back for a Software Engineer role at Uber. Nothing behavioral, just pure algorithms the whole way through. The problems ranged from a greedy assignment problem to a tree edge-reversal thing I hadn't seen before, plus a classic binary matrix search.
  • You have n tasks that must be split between two workers. Worker 1 must get exactly k tasks. Each task has a reward value depending on which worker does it. How do you assign tasks to maximize total reward?
  • Given a directed tree (connected, acyclic if you ignore directions) with n nodes and n-1 edges, find the minimum number of edge reversals needed so that every node has a directed path to some single root node r. Minimize over all possible choices of r.
  • Given a binary matrix where every row is sorted (all 0s before all 1s), find the index of the leftmost column that contains at least one 1. Your solution must be faster than O(m*n).

“The greedy insight is to think about the 'cost' of giving a task to worker 1 versus worker 2, specifically the difference reward1[i] minus reward2[i].”

View Post

Uber·Data Scientist·Technical Phone Screen

May 2026
Uber data science interview with a stats-heavy question about comparing proportions across two coins with very different sample sizes. Pretty standard hypothesis testing territory but the sample size imbalance adds a wrinkle that trips people up if they're not careful.
  • You have two coins: Coin A flipped 100 times with 40 heads, and Coin B flipped 1000 times with 600 heads. Are the head probabilities statistically different? Walk through your hypotheses, the right test, the test statistic, a 95% confidence interval for the difference, and any caveats around the unequal sample sizes.

“This is a two-proportion z-test setup and I knew that pretty quickly, but I fumbled the confidence interval formula under pressure.”

View Post

Uber·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Uber for a software engineering role, focused entirely on designing the shopping cart service for a food delivery app. Pretty intense scope, lots of moving parts, and they pushed hard on the tradeoffs.
  • Design the shopping cart service for an Uber Eats-style food delivery application.
  • How would you handle two devices editing the same cart simultaneously, and what are the tradeoffs between optimistic and pessimistic concurrency control here?
  • If a menu item's price changes while a user is in the middle of checking out, how does your system handle that?
  • Walk me through your storage choices for the cart service, considering write-heavy workloads and low-latency read requirements.
  • How would you design the API for the cart service, and how do you ensure idempotency for client retries?
  • How would you apply promotions, coupons, taxes, and delivery fees at checkout, and where does that logic live in your architecture?

“The core question.”

View Post

Uber·Software Engineer·Onsite - System Design / Architecture

May 2026
Uber system design round for a software engineer role. One question on trending items for an e-commerce landing page, and I came out of it feeling like I'd talked myself into a corner by nerding out on scoring algorithms when the interviewer clearly wanted to hear about the infrastructure side.
  • Design a 'trending items' feature for an e-commerce landing page. How would you handle caching, popularity scoring, and the tradeoff between freshness and relevance?

“I went deep on the scoring model early, like way too deep.”

View Post

Uber·Software Engineer·Onsite - Coding / Algorithms

May 2026
Uber SWE coding round, got a grid-based word search problem that looked manageable until I realized brute force wasn't going to cut it with 30k words in the input.
  • Given a 2D character grid and a list of words, find all words that can be formed by traversing horizontally or vertically adjacent cells without reusing any cell in a single word's path.

“My first instinct was to just DFS from every cell for every word, which works but completely falls apart when you have tens of thousands of words.”

View Post

Uber·Machine Learning Engineer·Technical Phone Screen

May 2026
Went through a technical screen for an MLE role at Uber focused entirely on ML concepts. Four questions back to back, all conceptual, no coding. Felt like a theory exam more than a conversation.
  • In gradient-boosted decision trees, how does maximum tree depth affect bias, variance, and overfitting risk? How does it impact training and inference cost, and how would you pick it in practice?
  • In neural networks, compare L1 regularization, L2 regularization, and weight decay. How does each one change the loss objective, the gradient updates, and the resulting weights?
  • After using dropout during training, what do you need to do differently at inference time, and why?
  • Define training versus inference for ML models. How do the data flows differ, what role does randomness play in each, and what are the performance tradeoffs to consider?

“This one I actually felt okay about.”

View Post

Uber·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Uber, one question focused on large-scale event logistics for the core rides product. Pretty open-ended, which I wasn't fully prepared for.
  • How would you improve the pickup and drop-off experience on Uber during large-scale events with crowds exceeding 10,000 people?

“I jumped straight into supply-demand balancing and surge pricing adjustments, which in hindsight felt too obvious.”

View Post

Uber·Machine Learning Engineer·Technical Phone Screen

May 2026
Uber MLE phone screen, pretty much one coding problem the whole time with a follow-up discussion about scale. Not a brutal round but the scale conversation at the end caught me more off guard than the actual coding.
  • Given a string of digits (2-9), return all possible letter combinations using the standard phone keypad mapping. Then discuss how output size scales for a 9-digit input and how you'd handle streaming the result.

“Backtracking felt straightforward and I got through it fine.”

View Post