Uber Interview Questions

Uber Coding & Algorithms Interview Questions

Practice 303 real Uber interview questions for 2026. Covers top categories — Coding & Algorithms, Analytics & Experimentation, Machine Learning, Behavioral & Leadership, Statistics & Math — across Software Engineer, Data Scientist, Machine Learning Engineer, and Technical Program Manager roles. Real questions from actual interviews with detailed solutions, focused guidance, and concrete interview preparation so you can practice the exact problem types Uber asks. Expect a coding-heavy loop for software engineering candidates: timed algorithm problems, online-assessment style OAs, and system-design tasks. For Software Engineer roles the recurring technical themes here are algorithm puzzles (kth-smallest-in-BST, knight/grid and reversal problems, prime-ending path counts), OA-style coding questions, and product-oriented design prompts such as a pickup-area driver queue and global nearby-restaurant search. Data Scientist questions center on membership/discount experiments, cold-start restaurant ratings and their launch evaluation, driver-acceptance modeling, and marketplace-impact analyses. Machine Learning Engineer prompts focus on completion-rate gaps, implementing attention and regression models, feed-ranking and restaurant-recommendation design, and pickup-location optimization. TPM items emphasize delivery-address fixes, competitive product comparisons, and leadership stories. Prepare by timing practice coding, rehearsing marketplace case studies, building short model write-ups, and polishing STAR examples for behavioral rounds.

303 Questions 1 Company07.03.2026
Showing 20 results
Role
Uber logo
Uber
Medium
Data ScientistNew Grad Locked

Can one car serve all riders?

This question evaluates understanding of interval scheduling and conflict detection, testing skills in time-interval reasoning, sorting, and efficient...

Coding & Algorithms
6
0
71 people solved
Apr 6, 2026
Uber logo
Uber
Medium
Software Engineer

Simulate views on an n-ary tree

Given a rooted, ordered n-ary tree (each node has a value and an ordered list of children), simulate an observer who starts at the bottom-left, moves ...

Coding & Algorithms
4
0
74 people solved
Sep 6, 2025
Uber logo
Uber
Medium
Software Engineer Locked

Design structure for bottom-K customers by revenue

This question evaluates data structure design and algorithmic complexity for dynamic order-statistics and update operations, focusing on maintaining c...

Coding & Algorithms
7
0
78 people solved
Oct 29, 2025
Uber logo
Uber
Hard
Software Engineer Locked

Solve Two OA Algorithm Problems

These problems evaluate algorithmic optimization and graph connectivity competencies, specifically cost-constrained integer resource allocation for th...

Coding & Algorithms
2
0
49 people solved
Jan 7, 2026
Uber logo
Uber
Medium
Technical Program ManagerSenior+

Fix unclear delivery addresses

A customer does not receive a delivery because the delivery address is unclear. As a product or operations leader, explain how you would diagnose the ...

Product / Decision Making
6
0
67 people solved
May 1, 2025
Uber logo
Uber
Medium
Technical Program ManagerSenior+

Answer core behavioral questions

You are interviewing for a Senior Program Manager role at Uber. Prepare strong, concise answers to these behavioral questions: 1. Why Uber? 2. Why are...

Behavioral & Leadership
24
0
300 people solved
May 1, 2025
Uber logo
Uber
Medium
Software Engineer

Produce a valid deployment order

You are given N services and a list of dependency pairs (A, B) meaning service B must be deployed before service A. Compute any valid deployment order...

Coding & Algorithms
15
0
104 people solved
Aug 12, 2025
Uber logo
Uber
Hard
Data Scientist

Define market-only rider experience metrics

Market-only Rider Experience Metrics and Market Balance Index (MBI) You are designing a metric suite for a rides marketplace where "rider experience" ...

Analytics & Experimentation
11
0
80 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Investigate ride declines and test free trials

LA Shared Rides Down 10% MoM — Diagnostic And Action Plan Context: The Los Angeles market is seeing a 10% month-over-month decline in completed rides ...

Analytics & Experimentation
10
1
73 people solved
Oct 13, 2025
Uber logo
Uber
Medium
Data Scientist

Analyze Cancellation Change with Statistics

A/B change in cancellation rate (before vs after) Context: You are evaluating a small product tweak intended to reduce cancellations. Treat each trip ...

Statistics & Math
6
0
97 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Design an A/B test for promo-targeting models

Experiment Design: Compare Two Ranking Models (M1 vs M0) for $5 Promotions Context You have two models, M0 (current) and M1 (new), that rank users for...

Analytics & Experimentation
7
0
102 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Evaluate impact without randomized experiments

Estimating a Promotion's Causal Effect Without an Experiment Context You need to estimate the causal impact of a marketing promotion on engagement (e....

Analytics & Experimentation
5
0
67 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Design an RCT for app-open discount

Design an RCT for an "X dollars off on app open" promotion in a two‑sided marketplace Context You operate a two‑sided marketplace mobile app (e.g., ri...

Analytics & Experimentation
7
0
107 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Design a robust email A/B test

A/B Test Design: New Email Subject Line for Weekly Campaign You manage a weekly email campaign to 10 million users. Baseline unique click-through rate...

Analytics & Experimentation
8
0
63 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Evaluate Push Notification Impact on Rideshare Supply Shortages

Evaluate Push Notification Impact on Rideshare Supply Shortages Experiment Design: Push Notifications for Airport Surge Shortage Resolution Context Wh...

Analytics & Experimentation
18
0
81 people solved
Aug 4, 2025
Uber logo
Uber
Medium
Software Engineer

Enforce ordered execution across threads

Enforce ordered execution across threads Implement a class with three methods alpha(), beta(), and gamma() that may be called by three different threa...

Coding & Algorithms
7
0
79 people solved
Aug 1, 2025
Uber logo
Uber
Medium
Software Engineer Locked

Solve Several Algorithm Problems

This collection evaluates proficiency in string processing (longest palindromic substrings), graph connectivity and union‑find style concepts for 2D p...

Coding & Algorithms
4
0
55 people solved
Mar 17, 2026
Uber logo
Uber
Medium
Software Engineer Locked

Design Global Nearby Restaurant Search

This question evaluates a candidate's ability to design a large-scale, low-latency distributed system that supports geospatial indexing, API and data ...

System Design
11
0
161 people solved
Mar 17, 2026
Uber logo
Uber
Medium
Data Scientist

Assess Cultural Fit and Leadership Potential in Candidates

Behavioral Phone Screen: Cultural Fit and Leadership Potential You are in a Data Scientist phone screen focused on cultural fit, leadership potential,...

Behavioral & Leadership
21
0
77 people solved
Jul 12, 2025
Uber logo
Uber
Medium
Data Scientist

Evaluate Email Subject Line Performance Using Hypotheses

Email Subject Line A/B Test: Hypotheses, CLT, and Sample Size An email marketing team wants to evaluate whether a new subject line improves click-thro...

Statistics & Math
16
0
72 people solved
Jul 12, 2025

Frequently Asked Questions

How hard are Uber interview questions?
Uber interview questions range from moderate to very challenging depending on role and level. Software Engineer loops emphasize data structures, algorithms and system-design problems that often require multi-step solutions and production tradeoffs, so expect mid-to-hard coding and design tasks. Data Scientist interviews skew toward analytics, experimentation and marketplace judgment with case-style product questions that reward rigorous causal thinking. Machine Learning Engineer rounds mix implementation and ML-system design, and TPM questions focus on program delivery and stakeholder tradeoffs. Preparation quality and interview level (junior vs senior) are the main determinants of perceived difficulty.
What is Uber's interview process and which roles ask these question types?
The typical Uber hiring sequence starts with a recruiter screen, followed by one or two technical phone or take-home assessments for some roles, then an onsite or virtual loop of four to six interviews and a hiring-committee review. Software Engineer rounds concentrate on coding, online assessment problems and system design. Data Scientist interviews emphasize SQL, experiments, product-analytics cases and behavioral ownership. Machine Learning Engineer interviews add ML model implementation and productionization questions. Technical Program Manager interviews focus on cross-functional program examples and behavioral leadership. Timelines usually span three to six weeks.
How long should I prepare for an Uber interview and how should I structure my timeline?
Plan your preparation based on role and current skill level. For Software Engineer positions allocate six to twelve weeks focusing on timed coding practice, mock interviews and system-design case work. Data Scientist candidates should budget three to six weeks prioritizing SQL, experimentation, product-case practice and clear storytelling about impact. Machine Learning Engineers need four to eight weeks combining model implementation, systems design and coding. TPM candidates can prepare in two to four weeks concentrating on program examples and stakeholder communication. Include final-week full mock loops and a review of role-specific Uber product scenarios.
Which specific subtopics should I focus on for each position at Uber?
For Software Engineers concentrate on algorithmic patterns seen in past Uber questions: BST kth-smallest, graph and knight problems, two online-assessment algorithm tasks, queue design for pickup-area driver matching, and product-oriented designs like global nearby-restaurant search and trade-off-driven design changes. Data Scientists should drill A/B testing, causal inference, cold-start rating models, marketplace evaluation (driver acceptance, membership and priority delivery impact) and translating analyses into action. Machine Learning Engineers should practice implementing attention, linear and logistic regression, feed ranking and ML-system tradeoffs including pickup-location optimization. TPMs should rehearse unclear-address resolution and cross-product comparisons with crisp ownership stories.
What standout tips and common pitfalls should I know before interviewing at Uber?
Standout tips: always clarify requirements and constraints up front, structure answers around measurable metrics and business impact, and narrate tradeoffs between latency, cost and accuracy. Use concrete examples from marketplace contexts and quantify outcomes when possible. For coding, write clean, testable code and discuss complexity and edge cases. Common pitfalls include skipping assumptions, failing to justify metric choices in product/analytics problems, neglecting production implications for ML designs, and overfitting to toy solutions instead of addressing scale and reliability. End with clear next steps or monitoring plans to show ownership.

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