practice a little every day, paws crossed ♡
Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 4291–4320 of 4616 results
- UberHardData ScientistFormulate hypotheses and compute AB test significance
This question evaluates a data scientist's competency in experimental design and statistical inference for A/B testing, covering hypothesis formulation, difference-in-proportions testing and confidence intervals, guardrail analysis, multiple-testing correction, interim alpha-spending approaches, and variance-reduction techniques such as CUPED.
Statistics & MathTechnical Screen - UberMediumSoftware EngineerGroup strings that are anagrams
This problem tests your ability to efficiently group strings by their character composition using hash maps or sorting techniques.
Coding & AlgorithmsTechnical ScreenHash MapStringSorting - UberEasyData ScientistHow to experiment on ETA reduction
This problem tests the ability to design an A/B test for product improvements and address potential confounding factors like selection bias and spillover effects.
Analytics & ExperimentationTechnical ScreenA/B TestingCausal InferenceMarketplace Dynamics - UberMediumData ScientistHow would you build UberEats ranking?
This question assesses the ability to design an end-to-end ranking system for a food delivery platform, focusing on objective formulation, feature selection, and the trade-offs between relevance and business constraints.
Machine LearningTechnical ScreenRecommender SystemsLearning to RankFeature Engineering - UberEasyData ScientistHow would you design an ETA prediction system?
This problem tests the ability to design an end-to-end machine learning system for predicting ride arrival times, focusing on feature selection, model architecture, and evaluation strategies.
Machine LearningTechnical ScreenML System DesignRegressionFeature Engineering - UberMediumMachine Learning EngineerImplement 1D convex minimization in Python
An Uber Machine Learning Engineer technical-screen question on derivative-free 1D convex optimization: minimize a black-box convex function on a closed interval using only function evaluations. It tests method selection (golden-section vs ternary search), stopping criteria and tolerances, function-evaluation and time/space complexity analysis, and robust handling of flat regions, boundary optima, noisy evaluations, and numerical precision.
Machine LearningTechnical Screen - UberMediumSoftware EngineerImplement 2D word search variants and analyze complexity
This question evaluates proficiency in grid-based string search, traversal techniques (fixed-direction scanning versus flexible-path exploration), algorithm design including backtracking/DFS, and the ability to analyze time and space complexity while accounting for redundancy and edge cases.
Coding & AlgorithmsTechnical Screen - UberMediumMachine Learning EngineerImplement CLIP Contrastive Loss
This question evaluates understanding and implementation of contrastive representation learning concepts—specifically similarity matrices, symmetric image-text contrastive loss, normalization, temperature scaling, and label construction—testing competency in building losses for embedding alignment in the Machine Learning domain.
Machine LearningTechnical Screen - UberMediumSoftware EngineerImplement Last-Click Attribution APIs
This problem tests your ability to process streams of events to perform stateful attribution, requiring efficient data structures to track user sessions and match conversions to the most recent preceding click.
Coding & AlgorithmsTechnical ScreenData StructuresAlgorithmsState Management - UberMediumMachine Learning EngineerImplement Multi-Head Self-Attention
This question tests the ability to implement the multi-head self-attention mechanism from scratch using PyTorch, focusing on tensor manipulation, linear projections, and the scaled dot-product attention formula.
Machine LearningTechnical ScreenPyTorchDeep LearningTransformers - UberMediumSoftware EngineerImplement Store Autocomplete
This problem tests proficiency with prefix-based data structures and efficient string retrieval, specifically requiring a design that optimizes for frequent search operations over a static list of store names.
Coding & AlgorithmsTechnical ScreenTrieString ManipulationData Structures - UberMediumMachine Learning EngineerImplement linear and logistic regression
This question evaluates understanding and hands-on implementation of linear and logistic regression, testing competencies in prediction functions, loss formulation, gradient computation, gradient descent training, regularization, and model selection within the Machine Learning domain (supervised learning and statistical modeling).
Machine LearningTechnical Screen - UberMediumData ScientistImplement weighted sampling without replacement
This question evaluates a candidate's ability to design numerically stable, streaming weighted-sampling methods and tests competencies in randomized algorithms, probability, numerical analysis, and memory/time trade-offs.
Coding & AlgorithmsTechnical Screen - UberHardData ScientistImprove Estimated Time of Arrival for Uber Riders
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Improve Estimated Time of Arrival for Uber Riders states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - UberHardSoftware EngineerMaintain the First Value That Appears Exactly Once
Practice a Uber coding interview problem focused on maintain the first value that appears exactly once. The prompt emphasizes edge cases, clean implementation, and verifiable test behavior without revealing the solution.
Coding & AlgorithmsTechnical Screen - UberMediumSoftware EngineerMaximize Throughput and Count Trigger Components
This problem tests your ability to optimize resource allocation using a greedy strategy and to identify connected components in a grid-based graph using traversal techniques.
Coding & AlgorithmsTechnical ScreenGreedyGraphsBFS/DFS - UberEasyData ScientistMeasure feature impact with switchback, PSM, and CACE
This question evaluates proficiency in causal inference and experimental design for product metrics, specifically testing knowledge of switchback experiments, time-series adjustments (seasonality and autocorrelation), propensity score methods for observational comparisons, and complier-average causal effect (LATE/CACE) estimation; it belongs to the Analytics & Experimentation and Data Science domain. It is commonly asked because it probes handling of real-world complications—time and city heterogeneity, non-compliance, and derived-metric inference—and tests both conceptual understanding of causal assumptions and practical application of statistical adjustment and inference.
Analytics & ExperimentationTechnical Screen - UberHardData ScientistMeasure rider incentive causal ROI
This question evaluates a data scientist's competency in causal inference and experimental design, marketplace health metric construction, ROI estimation, and the handling of selection bias, spillover effects, multiple testing, and heterogeneity in two-sided platforms.
Statistics & MathTechnical Screen - UberMediumSoftware EngineerMinimum Rooms, Merged Busy Blocks and Conflict Handling for Meeting Intervals
A multi-part coding question built on the classic meeting-room scheduling problem: find the minimum number of rooms for a set of meetings, merge overlapping meetings into busy blocks, and detect and handle booking conflicts. It tests interval ordering, endpoint conventions, sweep reasoning and efficient lookups.
Software Engineering FundamentalsTechnical Screen - UberMediumData ScientistMove zeros to front
This problem tests your ability to manipulate array indices in-place to reorder elements according to a specific condition while maintaining relative stability.
Coding & AlgorithmsTechnical ScreenArraysTwo-PointersIn-place - UberHardData ScientistNavigate urgency, priorities, and conflict
This question evaluates a candidate's ability to manage ambiguity, prioritize competing projects and deadlines, navigate cross-team dependencies, influence stakeholders without formal authority, and quantify outcomes and process improvements.
Behavioral & LeadershipTechnical Screen - UberMediumMachine Learning EngineerPresent a Marketplace ML Project Deep Dive
This question evaluates a candidate's competence in end-to-end ML system design, product-oriented machine learning, and cross-functional skills such as feature engineering, model deployment, online experimentation, monitoring, stakeholder communication, and measuring business impact.
ML System DesignTechnical Screen - UberMediumSoftware EngineerPrint directory tree with indentation
This question evaluates proficiency with representing and traversing hierarchical directory trees and producing depth-based formatted output, testing concepts such as tree traversal, recursion or iterative depth tracking, and string formatting for indentation.
Coding & AlgorithmsTechnical Screen - UberMediumSoftware EngineerProduce a valid deployment order
This question evaluates proficiency in graph algorithms and dependency resolution, including concepts such as topological ordering, cycle detection, and complexity analysis, plus systems-level reasoning about service deployment dependencies.
Coding & AlgorithmsTechnical Screen - UberMediumBackend EngineerReplace Dashes With Nearest Letters
This problem tests your ability to perform a multi-source shortest path search on a grid to determine the influence of characters on empty spaces, using Manhattan distance and lexicographical tie-breaking.
Coding & AlgorithmsTechnical ScreenBFSGridGraph - UberMediumSoftware EngineerReturn a Package Build Order
This question evaluates a candidate's skills in dependency resolution, graph traversal and cycle detection while reasoning about an externally discovered dependency subgraph and ordering constraints.
Coding & AlgorithmsTechnical Screen - UberMediumSoftware EngineerReturn the Earliest ID Seen Exactly Once
Maintain the earliest single-visit ID with counts and queue pruning, handling permanent duplicates, empty results, and strict versus amortized constant-time queries.
Coding & AlgorithmsTechnical Screen - UberMediumData ScientistShould Uber double member discounts?
This problem tests your ability to design a controlled experiment for a two-sided marketplace and evaluate whether a discount-doubling strategy is statistically and economically viable.
Statistics & MathTechnical ScreenA/B TestingCausal InferenceMarketplace Economics - UberMediumFrontend EngineerSimulate a Rank-Based Tournament
This question tests the ability to implement a single-elimination tournament simulation using queue-based structures and demonstrates competency in designing a relational database schema for tournament record-keeping.
Coding & AlgorithmsTechnical ScreenArrayQueueSimulation - UberHardSoftware EngineerSmallest Palindromic Number Strictly Greater Than n, Up to 18 Digits
Given a positive integer of up to 18 digits as a string, return the smallest palindromic number strictly greater than it, or the input itself when no such palindrome stays within 18 digits. Tests digit-level reasoning and edge cases such as palindromic inputs, carries and changes in digit count.
Coding & AlgorithmsTechnical Screen
Uber