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

Design a meeting scheduler and shopping cart

This question evaluates system design skills across distributed systems, API design, data modeling, indexing, caching, concurrency control, consistenc...

System Design
38
0
546 people solved
Mar 1, 2026
Uber logo
Uber
Hard
Data Scientist

Design a switchback and choose block length

Switchback Experiment Design: Airport Pickup Pricing with Spillovers You are a data scientist designing a switchback (time-based A/B) experiment to ev...

Analytics & Experimentation
28
0
256 people solved
Oct 13, 2025
Uber logo
Uber
Medium
Data Scientist

Design airport dispatch with ETA uncertainty

You control airport pickups with streaming ETAs for arriving flights and live driver locations/queues. Design an online dispatch algorithm that minimi...

Coding & Algorithms
14
0
102 people solved
Oct 13, 2025
Uber logo
Uber
Medium
Software Engineer

Describe your strongest project

What is the most impressive project you have worked on? In your answer, cover: - The problem/business goal and why it mattered - Your specific role an...

Behavioral & Leadership
9
0
91 people solved
Feb 20, 2026
Uber logo
Uber
Hard
Data Scientist

Optimize Surge Notifications for Rideshare Drivers

Optimize Surge Notifications for Rideshare Drivers Scenario A rideshare marketplace experiences airport demand spikes. When demand exceeds supply, the...

Machine Learning
96
0
264 people solved
Aug 4, 2025
Uber logo
Uber
Medium
Machine Learning Engineer Locked

Implement Multi-Head Self-Attention

This question evaluates understanding of multi-head self-attention and the competency to implement transformer attention modules using learned Q/K/V p...

Machine Learning
13
0
95 people solved
Jan 10, 2026
Uber logo
Uber
Medium
Data ScientistIntern

Design Rideshare Marketplace Causal Analyses

You are a data scientist at a ride-hailing marketplace. Answer the following case prompts as if you were advising product, operations, and marketplace...

Analytics & Experimentation
2
0
19 people solved
Feb 18, 2026
Uber logo
Uber
Easy
Data ScientistIntern

Design an Uber feature and analyze safety

You are interviewing for a Data Scientist summer internship at a ride-sharing marketplace. Part A: Product case Uber wants ideas for a new rider-facin...

Analytics & Experimentation
8
0
54 people solved
Jan 8, 2026
Uber logo
Uber
Medium
Data Scientist

Evaluate ETA Impact on Conversion

You are a Senior Data Scientist at a ride-hailing company such as Uber. ETA refers to the estimated pickup time shown to a rider before they decide wh...

Analytics & Experimentation
18
0
175 people solved
Feb 14, 2026
Uber logo
Uber
Easy
Data ScientistIntern

Analyze the Accident-Rate Spike

A monthly line chart shows the accident rate for Uber trips in one city. The accident rate increases sharply from June through November, then drops qu...

Statistics & Math
15
0
126 people solved
Feb 12, 2026
Uber logo
Uber
Hard
Data Scientist

Demonstrate Leadership in Ambiguous Analytics Projects

Behavioral & Leadership: End-to-End Analytics Project Under Ambiguity and Time Pressure Context You are a Data Scientist interviewing for a technical ...

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

Select the better $5 promo-targeting model

Coupon Targeting Under a Daily Budget: Policy, OPE, Calibration, and Monitoring Context - You have two user-scoring models for a $5 coupon: M0 (curren...

Machine Learning
9
0
71 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Design an ETA experiment under interference

Experiment Design: Estimating Causal Impact of a New Rider ETA Model in a Two-Sided Marketplace Context You are testing a new rider ETA model that cha...

Analytics & Experimentation
17
0
146 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist

Design an Uber A/B experiment end-to-end

Experiment Design: Pickup ETA Card Redesign Context: After a rider requests a trip, the app shows a pickup ETA card. The hypothesis is that clearer ET...

Analytics & Experimentation
29
0
275 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Machine Learning Engineer

Design real-time grid ETA for drivers

Real-Time Grid-ETA System Design You are tasked with designing a real-time system that maintains the remaining ETA for every driver currently located ...

ML System Design
22
0
254 people solved
Sep 6, 2025
Uber logo
Uber
Medium
Machine Learning Engineer

Implement 1D convex minimization in Python

Question Implement, in Python, an algorithm that minimizes a 1D black-box convex function F(x) over a closed interval [a, b]. Assume F is convex (henc...

Machine Learning
20
0
168 people solved
Sep 6, 2025
Uber logo
Uber
Medium
Data ScientistNew Grad Locked

Implement FizzBuzz

This question evaluates basic programming and algorithmic reasoning, including control flow, modular arithmetic, string handling, and the ability to a...

Coding & Algorithms
4
0
64 people solved
Mar 22, 2026
Uber logo
Uber
Medium
Data ScientistSenior+ Locked

How would you build UberEats ranking?

This question evaluates machine learning and recommender-systems competencies for ranking in a food delivery marketplace, covering problem formulation...

Machine Learning
8
0
54 people solved
Jan 22, 2026
Uber logo
Uber
Medium
Software Engineer Locked

Count Islands After Land Additions

This question evaluates understanding of dynamic connectivity, incremental graph updates, and algorithmic efficiency when maintaining connected compon...

Coding & Algorithms
1
0
15 people solved
May 11, 2026
Uber logo
Uber
Medium
Software Engineer

Develop test plan and TDD for word search

Design a Comprehensive Test Plan and TDD Workflow for Word-Search Functions Context and Assumptions We are testing two grid-based word search function...

Software Engineering Fundamentals
5
0
49 people solved
Sep 6, 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.

Explore more Uber interview questions

Jump straight to Uber questions for a specific role or category.

By role
By category
In-depth guides
Across all companies