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

Design a search autocomplete system

Design a Search Autocomplete System Design a planet-scale search autocomplete (type-ahead) service. As a user types into a search box, the service ret...

System Design
16
0
220 people solved
Jul 15, 2025
Uber logo
Uber
Medium
Data Scientist

Design a Ride-Hailing ETA System

You are a Data Scientist at a ride-hailing company. Design an ETA system used in the rider and driver apps to estimate both pickup ETA and trip ETA. D...

Machine Learning
7
0
72 people solved
Jan 14, 2026
Uber logo
Uber
Medium
Data Scientist

How to evaluate lowering ETA?

Uber wants to estimate the business value of reducing ETA, where ETA is the predicted time from when a rider requests a trip until the driver arrives ...

Analytics & Experimentation
14
0
94 people solved
Feb 1, 2026
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
547 people solved
Mar 1, 2026
Uber logo
Uber
Medium
Frontend Engineer

Design a Scalable Calendar Service

Design an online calendar service (think Google Calendar) that supports both individual and shared calendars. Users must be able to create, update, de...

System Design
12
0
90 people solved
May 4, 2026
Uber logo
Uber
Medium
Data ScientistNew Grad

Describe Conflict and Impact

Prepare strong answers for the following behavioral and project deep-dive questions for a data scientist role: 1. Tell me about a time you went beyond...

Behavioral & Leadership
10
0
71 people solved
Feb 27, 2026
Uber logo
Uber
Medium
Machine Learning Engineer

Explain XGBoost depth, regularization, and dropout

ML Conceptual Questions (Onsite) Answer the following: (a) Gradient-boosted decision trees: How does maximum tree depth affect bias/variance, overfitt...

Machine Learning
8
0
120 people solved
Sep 6, 2025
Uber logo
Uber
Hard
Software Engineer

Compare Paxos and Raft

Consensus over a Replicated Log: Paxos vs. Raft Context You are designing a fault-tolerant replicated log to back a stateful service (e.g., a key-valu...

System Design
17
0
146 people solved
Sep 6, 2025
Uber logo
Uber
Medium
Software Engineer Locked

Evaluate an Arithmetic Expression

This question evaluates proficiency in parsing arithmetic expressions, handling unary and binary operators, parentheses, whitespace, numeric tokenizat...

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

Design Stock Price Alerts

Design a stock price alert system that allows users to create alerts for securities and receive notifications when the market price crosses a specifie...

System Design
12
0
224 people solved
Dec 28, 2025
Uber logo
Uber
Medium
Software Engineer

Design a pickup-area driver queue

Design an internal service for a ride-hailing company that maintains a driver queue for each pickup area, such as an airport pickup zone or staging lo...

System Design
26
0
239 people solved
Feb 22, 2026
Uber logo
Uber
Medium
Software Engineer

Solve BFS and grid tasks

The coding rounds included several algorithmic problems: 1. Threshold search with binary search: You are given floors 1..n and an API canOperate(floor...

Coding & Algorithms
12
0
91 people solved
Feb 22, 2026
Uber logo
Uber
Hard
Data Scientist

Measure rider incentive causal ROI

Rider Incentive Targeting: Causal Incrementality, ROI, and Spillovers Context: You plan a rider‑side incentive (e.g., “20% off up to $10”) targeted by...

Statistics & Math
6
0
100 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Software Engineer

Design real-time driver heatmap system

System Design: Real-Time Driver Heatmap and Top-K Hottest Cells Context You are building a real-time driver heatmap for a ride-hailing platform that v...

System Design
87
0
938 people solved
Aug 1, 2025
Uber logo
Uber
Medium
Data ScientistNew Grad Locked

Evaluate marketplace interventions

This question evaluates a data scientist's competency in product analytics, causal inference, and experimentation design for two-sided marketplaces, f...

Analytics & Experimentation
13
0
96 people solved
Mar 22, 2026
Uber logo
Uber
Easy
Data ScientistIntern

Design and Test a New Feature

You are interviewing for a Data Scientist internship at Uber. Assume the Uber rider app already includes standard functionality such as booking a ride...

Analytics & Experimentation
20
0
137 people solved
Feb 12, 2026
Uber logo
Uber
Hard
Software Engineer

Design a ride-hailing platform like Uber

You are asked to design a large-scale ride-hailing platform similar to Uber. Requirements Functional requirements - Riders can: - Sign up, log in, a...

System Design
24
0
168 people solved
Nov 17, 2025
Uber logo
Uber
Medium
Software Engineer Locked

Answer Common Behavioral Questions

This question evaluates interpersonal communication, conflict resolution, leadership, self-awareness, and project ownership skills within the Behavior...

Behavioral & Leadership
13
0
90 people solved
Mar 17, 2026
Uber logo
Uber
Medium
Software Engineer Locked

Choose root to minimize edge reversals

This question evaluates understanding of directed versus undirected graph representations, tree rooting, and optimization of edge orientations, testin...

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

Design top-K tracker with linked lists

Design top-K tracker with linked lists Design a data structure that tracks the top-K most frequent keys in a stream. Support operations increment(key)...

Coding & Algorithms
11
0
95 people solved
Aug 1, 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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