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
Software Engineer Locked

Deep Equality of Two Records

This coding question tests a software engineer's ability to implement recursive structural comparison across nested, heterogeneous data types. It eval...

Coding & Algorithms
1
0
12 people solved
Jun 23, 2026
Uber logo
Uber
Medium
Frontend Engineer Locked

Build a React Parking Lot Manager

This question evaluates frontend engineering skills in React, including functional components and hooks, client-side state management, UI design, algo...

Software Engineering Fundamentals
22
0
182 people solved
May 4, 2026
Uber logo
Uber
Hard
Software Engineer Locked

Schedule Non-Overlapping Meetings Efficiently

This question evaluates understanding of interval scheduling and overlap detection, proficiency in selecting efficient data structures for dynamic ord...

Coding & Algorithms
8
0
64 people solved
May 2, 2026
Uber logo
Uber
Medium
Software Engineer

Design cart and pricing engine classes

Design the core classes and interfaces for a cart & pricing engine for a food delivery marketplace (e.g., Uber Eats). This is an object-oriented / low...

System Design
32
0
364 people solved
Jan 11, 2026
Uber logo
Uber
Medium
Data ScientistNew Grad Locked

Evaluate Promotions for Uber Eats Users

This question evaluates a data scientist's causal inference and experimentation competencies—including randomized trial design, treatment-effect estim...

Machine Learning
73
0
553 people solved
Apr 30, 2026
Uber logo
Uber
Medium
Software EngineerSenior+

Design cart management lifecycle service

Scenario You are designing the backend for an on-demand delivery app (restaurants and grocery). Users can create a cart, modify items from multiple de...

System Design
140
0
1029 people solved
Mar 1, 2026
Uber logo
Uber
Medium
Machine Learning Engineer

Describe an ML system you built

Describe a machine learning system that you previously designed, built, or owned. Cover the problem statement, business goal, data sources, feature en...

Behavioral & Leadership
5
0
77 people solved
Mar 1, 2026
Uber logo
Uber
Medium
Machine Learning Engineer

Design a feed ranking system

Design a machine-learning-based feed ranking system for a consumer product. The system should rank candidate posts or items for a user's home feed in ...

ML System Design
10
0
197 people solved
Mar 1, 2026
Uber logo
Uber
Easy
Machine Learning EngineerIntern

Explain and test completion-rate gaps

In a food delivery marketplace, alcohol-related orders have a lower order completion rate than non-alcohol orders. Answer the following: 1. Propose se...

Machine Learning
24
0
273 people solved
Mar 9, 2026
Uber logo
Uber
Medium
Software EngineerSenior+

Group Anagrams

Group Anagrams Given an array of strings words, group together all strings that are anagrams of one another and return the groups. Two strings are ana...

Coding & Algorithms
1
0
10 people solved
Jun 28, 2026
Uber logo
Uber
Medium
Data ScientistNew Grad

Describe ownership and failure

Answer the following behavioral questions in a structured way, using specific examples from your past work or research: 1. Tell me about a time you we...

Behavioral & Leadership
11
0
99 people solved
Mar 22, 2026
Uber logo
Uber
Medium
Software EngineerSenior+ Locked

Implement stream queries and bounded-difference subarrays

This question evaluates proficiency in data structures and algorithmic problem-solving, covering streaming uniqueness tracking (first-non-repeated ele...

Coding & Algorithms
9
0
56 people solved
May 8, 2026
Uber logo
Uber
Medium
Data Scientist

Compare Two Coin Proportions

You are given results from two independent coin-toss experiments: - Coin A was tossed 100 times and landed heads 40 times. - Coin B was tossed 1,000 t...

Statistics & Math
12
0
104 people solved
Mar 28, 2026
Uber logo
Uber
Medium
Data Scientist

Design Pricing Model Experiment

You work as a data scientist for a ride-hailing marketplace. The company wants to launch a new pricing model that may change the price shown to riders...

Analytics & Experimentation
4
0
73 people solved
Mar 28, 2026
Uber logo
Uber
Hard
Software Engineer Locked

Build Quadtree and Find Grid Words

This question evaluates divide-and-conquer tree construction and recursive spatial data structures for the quadtree task, and grid-based search and ba...

Coding & Algorithms
7
0
84 people solved
Jan 26, 2026
Uber logo
Uber
Medium
Software Engineer Locked

Design A URL Shortener

This question evaluates skills in large-scale system design, covering distributed systems, API design, data modeling and storage, caching, short-code ...

System Design
18
0
134 people solved
May 4, 2026
Uber logo
Uber
Medium
Software EngineerSenior+

Describe a Trade-off Design Change

Tell me about a time when you changed a system design because of an important trade-off. Explain the original design, the competing goals or constrain...

Behavioral & Leadership
10
0
178 people solved
Apr 16, 2026
Uber logo
Uber
Medium
Data ScientistNew Grad Locked

Build cold-start restaurant ratings

This question evaluates a data scientist's ability to design a production-ready predictive modeling approach for cold-start ratings, testing competenc...

Machine Learning
24
0
187 people solved
Apr 6, 2026
Uber logo
Uber
Medium
Machine Learning Engineer

Implement linear and logistic regression

Explain and implement linear regression and logistic regression from scratch. Your answer should cover: - The prediction function for each model - The...

Machine Learning
32
0
220 people solved
Mar 1, 2026
Uber logo
Uber
Easy
Data Scientist Locked

How do you derive CDF from a PDF?

This question evaluates understanding of the relationship between a probability density function and its cumulative distribution function, the formal ...

Statistics & Math
11
0
138 people solved
Feb 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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