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
Hard
Software Engineer

Prioritize rooms for allocation

System Design: Room Allocation and Ranking Service Context and assumptions: - The client sends a meeting request with: (a) time window or fixed start/...

System Design
18
0
121 people solved
Sep 6, 2025
Uber logo
Uber
Medium
Software Engineer Locked

Compute time for virus to infect grid

This question evaluates a candidate's ability to reason about grid-based state propagation, algorithmic problem solving, and performance consideration...

Coding & Algorithms
23
0
178 people solved
Feb 12, 2026
Uber logo
Uber
Medium
Software Engineer

Solve 12 coding interview problems

Below are multiple independent coding problems. --- Problem 1: Reduce an integer to 0 with \(\pm 2^i\) You are given a positive integer \(n\). In one ...

Coding & Algorithms
352
0
3689 people solved
Feb 12, 2026
Uber logo
Uber
Medium
Software Engineer Locked

Compute sums and max path in N-ary tree

This question evaluates implementation and traversal skills for N-ary tree data structures, including recursion/DFS techniques, aggregation of node va...

Coding & Algorithms
6
0
85 people solved
Feb 11, 2026
Uber logo
Uber
Medium
Software Engineer Locked

Count Valid Infection Orders

This question evaluates combinatorial enumeration and algorithmic reasoning by counting valid sequences of state changes in a linear infection process...

Coding & Algorithms
2
0
24 people solved
Apr 21, 2026
Uber logo
Uber
Medium
Data Scientist

Formulate OR model to reduce driver backtracking

Define and reduce driver ‘backtracking’ in a marketplace. First, define a quantitative backtracking metric B per driver-hour from GPS and assignment l...

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

Explain and validate A/B test assumptions

A/B Test Validity: Core Assumptions, Violations, Diagnostics, and Mitigations You are designing and evaluating an online A/B test for a large, multi-s...

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

Compute A/B sample size under clustering

A/B Test Sample Size With Unequal Allocation, Clustering, and Attrition Context You are planning a two-arm signup A/B test (binary outcome: convert vs...

Statistics & Math
15
0
140 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Diagnose and reduce first-action drop-offs

Funnel Drop‑Off: Instrumentation, Incentives, Fairness, and Ownership Context You lead a program where candidates must: (1) submit paperwork, then (2)...

Behavioral & Leadership
3
0
58 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Analyze results and large p-values correctly

Experiment Analysis Plan: User-Level ITT with Robust Inference, Variance Reduction, Ratios, Skew, Non-Compliance, and Decision Framework Context You r...

Statistics & Math
8
0
77 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Navigate urgency, priorities, and conflict

Behavioral & Leadership: Ambiguity, Dependencies, and Execution Under Pressure You will describe one real project where you faced high ambiguity and c...

Behavioral & Leadership
8
0
71 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Design an A/B test; choose Z vs T

A/B Test on a Signup Funnel: Sample Size, Test Choice, Sequential Design, and Causal Plan Context You are planning a two-variant A/B test on a signup ...

Statistics & Math
8
0
74 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Apply instrumental variables under interference

IV estimation for a ride‑sharing feature when A/B testing is infeasible due to interference Context You need to estimate the causal effect of a new ri...

Statistics & Math
10
0
99 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Design an experiment with marketplace network effects

Causal Experiment Design for a Two‑Sided Marketplace with Interference You are designing a causal experiment for a new networked product in a two‑side...

Analytics & Experimentation
6
0
71 people solved
Oct 13, 2025
Uber logo
Uber
Medium
Software Engineer Locked

Solve BST and Grid Query Problems

This question evaluates understanding of binary search tree order-statistics and grid reachability under threshold constraints, testing competencies i...

Coding & Algorithms
5
0
41 people solved
Apr 19, 2026
Uber logo
Uber
Medium
Software Engineer

Find a Bounded Subarray and Largest Square

Implement the following two independent coding tasks. Task A: Longest bounded-range subarray Given an integer array nums and an integer limit, return ...

Coding & Algorithms
1
0
14 people solved
Apr 12, 2026
Uber logo
Uber
Medium
Software Engineer Locked

Find all dictionary words in a grid

This question evaluates skills in grid traversal, string-search and pattern-matching techniques, recursive backtracking, and the efficient use of data...

Coding & Algorithms
15
0
141 people solved
Jan 22, 2026
Uber logo
Uber
Medium
Data ScientistNew Grad Locked

Should Uber double member discounts?

This question evaluates competency in causal inference, experimental design, statistical power and sample-size analysis, metric definition, and two-si...

Statistics & Math
19
0
143 people solved
Apr 6, 2026
Uber logo
Uber
Medium
Data ScientistNew Grad Locked

Evaluate a cold-start rating launch

This question evaluates a data scientist's competency in marketplace analytics, causal inference, experimentation design and measurement, specifically...

Analytics & Experimentation
20
0
281 people solved
Apr 6, 2026
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

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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