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 Locked

Evaluate business value of lower ETA

This question evaluates experimental design, causal inference, metric definition, statistical interpretation, and marketplace analytics in the context...

Analytics & Experimentation
5
0
43 people solved
Jan 18, 2026
Uber logo
Uber
Medium
Software Engineer Locked

Return sorted squares of a sorted array

This question evaluates array-processing and algorithmic optimization skills, including handling negative values, element-wise transformations, and pr...

Coding & Algorithms
7
0
77 people solved
Jan 10, 2026
Uber logo
Uber
Medium
Data Scientist Locked

Derive a CDF from a PDF

This question evaluates a candidate's understanding of the relationship between probability density functions and cumulative distribution functions, i...

Statistics & Math
5
0
41 people solved
Jan 3, 2026
Uber logo
Uber
Hard
Software Engineer

Find earliest time all riders become connected

You are given a time-ordered log of events involving Uber riders. Each event has a timestamp and two rider names. Event types: 1. t X shared-ride-with...

Coding & Algorithms
15
0
110 people solved
Jan 3, 2026
Uber logo
Uber
Hard
Software Engineer

Design driver heat map and discuss consensus

Design driver heat map and discuss consensus Real-Time Driver Heat Map with Top-K Busiest Cells, Plus Paxos vs. Raft Context You are designing a real-...

System Design
29
0
250 people solved
Aug 4, 2025
Uber logo
Uber
Hard
Data Scientist

Improve Estimated Time of Arrival for Uber Riders

Improve Estimated Time of Arrival for Uber Riders Scenario Ride-hailing platform: understanding and improving the Estimated Time of Arrival (ETA) show...

Analytics & Experimentation
9
0
114 people solved
Aug 4, 2025
Uber logo
Uber
Medium
Software Engineer

Design MapReduce for schedule aggregation

Design MapReduce for schedule aggregation MapReduce Design: Common Availability From Busy Intervals Context You are given large-scale calendar data: e...

System Design
6
0
96 people solved
Aug 1, 2025
Uber logo
Uber
Medium
Machine Learning Engineer

Choose K pickup locations minimizing L1 distance

Coding: K Shuttle Pickup Locations (L1) You are given the coordinates of N people on a 2D grid. You want to open K shuttle pickup locations (pickup po...

Coding & Algorithms
30
0
314 people solved
Dec 15, 2025
Uber logo
Uber
Easy
Data ScientistSenior+

Transform DataFrame and compute diff-in-diff

You are given a pandas DataFrame df with the following columns: - unit_id (string): entity identifier (e.g., user, city, driver) - group (string): eit...

Data Manipulation (SQL/Python)
18
3
246 people solved
Dec 11, 2025
Uber logo
Uber
Medium
Software Engineer

Detect cycle in directed dependency graph

You are given a directed dependency graph representing services in a system. - There are n services, labeled from 0 to n - 1. - You are given a list o...

Coding & Algorithms
6
0
90 people solved
Dec 8, 2025
Uber logo
Uber
Medium
Software Engineer

Compute exclusive execution time from logs

Problem You are given execution logs from a single-threaded CPU that runs n functions labeled 0..n-1. The CPU can run only one function at a time. A f...

Coding & Algorithms
9
0
92 people solved
Nov 27, 2025
Uber logo
Uber
Hard
Software Engineer Locked

Minimize time using elevator then climb stairs

This question evaluates a candidate's ability to model and optimize a discrete decision under resource constraints, testing algorithmic optimization, ...

Coding & Algorithms
10
0
94 people solved
Oct 18, 2025
Uber logo
Uber
Hard
Data Scientist Locked

Define ride success metric for Uber

This question evaluates skills in defining product-level KPIs, statistical validation, and experimental design for an on-demand mobility service, cove...

Analytics & Experimentation
11
0
93 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist Locked

Measure driver experience quantitatively

This question evaluates a data scientist's competencies in designing composite metrics, event-level aggregation, statistical validation, debiasing for...

Analytics & Experimentation
4
0
64 people solved
Oct 13, 2025
Uber logo
Uber
Medium
Data Scientist Locked

Derive paying users over time with churn

This question evaluates the ability to model user conversion and churn with discrete-time recurrence relations, derive closed-form expressions for pay...

Statistics & Math
5
0
74 people solved
Oct 13, 2025
Uber logo
Uber
Medium
Data Scientist

Compute ETA shift and conversion uplift

Use PostgreSQL (SQL) and brief Python pseudocode. Assume 'today' is 2025-09-01. Schema: - trips(trip_id BIGINT, request_ts TIMESTAMP, city_id INT, rid...

Data Manipulation (SQL/Python)
0
0
6 people solved
Oct 13, 2025
Uber logo
Uber
Medium
Data Scientist

Write SQL for fares and age-band counts

You have two tables. Schema: - drivers(driver_id VARCHAR PRIMARY KEY, name VARCHAR, date_of_birth DATE) - trips(trip_id VARCHAR PRIMARY KEY, driver_id...

Data Manipulation (SQL/Python)
3
1
43 people solved
Oct 13, 2025
Uber logo
Uber
Medium
Data Scientist

Check anagrams under real-world constraints

Given two strings s and t, determine whether they contain exactly the same multiset of characters (e.g., 'abc' and 'cab' → true; 'aab' and 'ab' → fals...

Coding & Algorithms
4
0
84 people solved
Oct 13, 2025
Uber logo
Uber
Hard
Data Scientist Locked

Design promo experiment and explain correlation

This question evaluates causal inference, observational data interpretation, and randomized experiment design skills in a two-sided marketplace contex...

Analytics & Experimentation
7
0
59 people solved
Sep 16, 2025
Uber logo
Uber
Medium
Software Engineer

Design top-K frequency structure

Design an in-memory data structure that supports: add(x) to observe an item, inc(x) to increment its frequency, dec(x) to decrement (deleting when zer...

Coding & Algorithms
6
0
62 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.

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