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 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
Easy
Machine Learning EngineerIntern

Analyze sales with groupby

You are given three pandas DataFrames representing a food delivery marketplace: - orders(order_id, product_id, quantity, unit_price, status) - product...

Coding & Algorithms
4
0
41 people solved
Mar 9, 2026
Uber logo
Uber
Medium
Software Engineer

Simulate fixed-shape tiling on a grid

You are given an empty n-by-m grid and a list of tile patterns, patterns[0..k-1]. Each pattern is a binary matrix (1 = filled cell, 0 = hole) and must...

Coding & Algorithms
9
0
95 people solved
Sep 6, 2025
Uber logo
Uber
Medium
Data ScientistNew Grad Locked

Model Driver Acceptance Probability

This question evaluates a candidate's competency in production machine learning system design and operationalization, including label definition and u...

Machine Learning
5
0
63 people solved
Feb 27, 2026
Uber logo
Uber
Medium
Data Scientist

Determine Sample Size for Promotion Campaign A/B Test

Determine Sample Size for Promotion Campaign A/B Test Scenario Uber plans to launch a promotion campaign and wants to evaluate its effectiveness with ...

Analytics & Experimentation
71
0
158 people solved
Aug 4, 2025
Uber logo
Uber
Hard
Data Scientist

Analyze T2 Results and Recommend Launch Strategy

Analyze T2 Results and Recommend Launch Strategy A/B Test Interpretation, Launch Decision, Segmentation, and Multi-Experiment Error Control Context Yo...

Analytics & Experimentation
93
1
259 people solved
Aug 4, 2025
Uber logo
Uber
Medium
Technical Program ManagerSenior+

Compare Uber Eats and DoorDash

Compare Uber Eats and DoorDash from a product and market strategy perspective. Which platform is better positioned, for which user segments, and why? ...

Product Design & Strategy
5
0
39 people solved
May 1, 2025
Uber logo
Uber
Medium
Software Engineer Locked

Compute sums and maximum path in a tree

This question evaluates understanding and implementation of binary tree traversal, recursion, aggregation of node values, and computation of maximum s...

Coding & Algorithms
5
0
74 people solved
Feb 11, 2026
Uber logo
Uber
Medium
Software Engineer

Determine balanced k values in a permutation

You are given a permutation p of the integers 1..n. For a number k (where 1 <= k <= n), call k balanced if there exists a contiguous subarray p[l..r] ...

Coding & Algorithms
33
0
272 people solved
Feb 11, 2026
Uber logo
Uber
Hard
Software Engineer

Describe end-to-end design of past project

Describe end-to-end design of past project System Design Deep Dive: Past Project End-to-End Provide a detailed, end-to-end walkthrough of a significan...

System Design
8
0
95 people solved
Jul 15, 2025
Uber logo
Uber
Medium
Data Scientist

Evaluate Rider-Incentive Program Impact with Key Metrics

Evaluate a Rider-Incentive Program in a Ride-Hailing Marketplace A ride-hailing team plans to launch a new rider-incentive program and needs to evalua...

Analytics & Experimentation
77
0
202 people solved
Jul 12, 2025
Uber logo
Uber
Hard
Data Scientist Locked

Differentiate Type I vs II errors under costs

This question evaluates understanding of hypothesis testing (Type I/II errors), cost-sensitive decision theory, sample size calculation for proportion...

Statistics & Math
12
0
90 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Estimate causal effect with interference

A/B Test With Noncompliance and Interference: Causal Effect of Surge Recommendations on Completed Trips Context You ran an A/B test that assigned some...

Analytics & Experimentation
23
0
179 people solved
Oct 13, 2025
Uber logo
Uber
Easy
Data Scientist Locked

How to experiment on ETA reduction

This question evaluates a data scientist's competence in causal inference, A/B test design, metric selection, and diagnosing observational confounding...

Analytics & Experimentation
16
0
203 people solved
Feb 6, 2026
Uber logo
Uber
Easy
Software Engineer

Solve three algorithmic optimization and search problems

You are given three independent coding problems. --- Problem 1: Allocate tasks between two workers to maximize reward You have n independent tasks tha...

Coding & Algorithms
13
0
94 people solved
Oct 7, 2025
Uber logo
Uber
Medium
Data Scientist Locked

Move zeros to the front

This question evaluates array manipulation and in-place algorithm competencies, including handling space-time trade-offs and preserving relative order...

Coding & Algorithms
4
0
38 people solved
Jan 30, 2026
Uber logo
Uber
Hard
Software Engineer Locked

Design Food Delivery Cart

This question evaluates a candidate's expertise in backend system design for transactional applications, covering competencies such as API design, dat...

System Design
11
0
75 people solved
Jan 26, 2026
Uber logo
Uber
Medium
Software Engineer Locked

Bracket substrings matching any pattern

This question evaluates string manipulation and pattern-matching competency, including substring search, prioritization rules for leftmost and longest...

Coding & Algorithms
8
0
77 people solved
Jan 22, 2026
Uber logo
Uber
Medium
Data ScientistSenior+ Locked

How would you evaluate UberEats growth?

This question evaluates product analytics, experimentation design, and causal inference competencies in the context of a food-delivery marketplace, em...

Analytics & Experimentation
6
0
52 people solved
Jan 22, 2026
Uber logo
Uber
Hard
Software Engineer Locked

Find robots matching obstacle-distance signature

This question evaluates spatial reasoning on 2D grids, the ability to compute distance signatures to nearest obstacles, and careful handling of bounda...

Coding & Algorithms
10
0
148 people solved
Jan 20, 2026

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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