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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
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- 11
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- 8
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Showing 4651–4680 of 9403 results
- LyftEasyData ScientistDetermine Probability of Single Ride on Following Day
Evaluates a binomial probability setup for a ride-pricing experiment with two independent offers. Strong answers model the number of reasonable prices as Binomial(2, P), compute exactly one success as 2P(1-P), and compute permanent-rider probability as P squared.
Statistics & MathTechnical Screen - LyftHardData ScientistDevelop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors
Evaluates dynamic-pricing system design for Lyft's two-sided marketplace. Strong answers separate prediction from control, include real-time demand and supply features, model elasticity and ETA response, optimize with fairness and stability constraints, and validate through simulation and experiments.
Machine LearningTechnical Screen - LyftMediumSoftware EngineerDiscuss eligibility and behavioral scenarios
This question evaluates behavioral and leadership competencies—including communication, conflict resolution, feedback reception, end-to-end ownership, prioritization, and employment logistics—within the Behavioral & Leadership domain for a Software Engineer role.
Behavioral & LeadershipOnsite - LyftEasyData ScientistExplain Bayes’ Theorem and P-Value in Decision-Making
Explain Bayes’ Theorem and P-Value in Decision-Making evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - LyftMediumSoftware EngineerExplain Project Ownership and Collaboration with Colleagues
Tests an engineering candidate's ability to demonstrate end-to-end technical ownership, cross-functional influence, and practical collaboration on a high-impact project.
Behavioral & LeadershipOnsiteBehavioralLeadershipOwnership - LyftMediumMachine Learning EngineerExplain Transformer and Fine-Tuning Basics
This question evaluates understanding of transformer architectures, embedding models, and transfer learning concepts such as pretraining and fine-tuning, testing competence in representation learning and model adaptation.
Machine LearningTechnical Screen - LyftHardData ScientistHow to Target Coupon Users
This question evaluates a data scientist's competency in causal inference, uplift and propensity modeling, user segmentation, and experimental design for targeted marketing campaigns.
Analytics & ExperimentationTechnical Screen - LyftHardData ScientistHow to target commute coupon users?
This question tests your ability to design an experiment to measure the incremental lift of a coupon program and your strategy for segmenting users based on commuting behaviors to maximize ROI.
Analytics & ExperimentationTechnical ScreenCausal InferenceExperimentationUser Segmentation - LyftHardSoftware EngineerImplement Grid Spread and Transactional Store
This question evaluates skills in grid-based propagation modeling and stateful transactional system design, testing concepts such as graph traversal and timed spread dynamics for the grid task and nested transaction semantics, isolation, and state management for the in-memory key-value store.
Coding & AlgorithmsOnsite - LyftMediumMachine Learning EngineerImplement K-Means Clustering from Scratch in Plain Python or NumPy
A live coding exercise that asks you to implement k-means clustering from scratch in Python, with NumPy optional, and make it pass the interviewer's test cases. It tests the assignment and update steps, initialization, convergence, empty clusters and writing runnable code under time pressure.
Machine LearningTechnical Screen - LyftMediumSoftware EngineerImplement Stateful Fetch-N over a Paginated Upstream
Implement a stateful fetchN wrapper over a paginated upstream that returns items in source order across repeated calls. Define buffering, page-token progression, exhaustion, empty pages, invalid input, and retry behavior without losing or duplicating items.
Software Engineering FundamentalsOnsite - LyftEasySoftware EngineerImplement a Key-Value Store with Nested Transactions
Implement an in-memory string key-value store with lookup, assignment, deletion, value counts, and deeply nested transactions. Define visibility plus exact begin, rollback, and inner or outer commit behavior without copying the entire database per transaction.
Coding & AlgorithmsOnsite - LyftMediumSoftware EngineerImplement command-driven in-memory key-value database
This question evaluates a candidate's ability to design efficient in-memory data structures and implement transactional semantics for operations like SET/GET/DELETE/COUNT, including handling nested transactions, rollbacks, and commits.
Coding & AlgorithmsOnsite - LyftMediumSoftware EngineerImplement paginated retrieval of transactions
Implement paginated retrieval of transactions evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - LyftMediumSoftware EngineerIn-Memory Key-Value Store with Nested Begin, Commit, and Rollback Transactions
A coding question to implement an in-memory key-value store with begin, set, unset, commit and rollback, where transaction blocks can be nested and committed changes can no longer be rolled back. It tests how state is recorded per block, restoring keys that were absent, nested commit semantics, and operation complexity.
Software Engineering FundamentalsOnsite - LyftMediumData ScientistInvestigate Causes of Driver WOW Score Drop
Investigate Causes of Driver WOW Score Drop evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - LyftMediumData ScientistInvestigate Metric Drops and Coupon Retention
This question tests your ability to diagnose a sudden drop in a core business metric, distinguish between technical tracking issues and product-driven behavior, and design a strategy to evaluate coupon effectiveness.
Analytics & ExperimentationTechnical ScreenProduct AnalyticsExperimentationCausal Inference - LyftMediumData ScientistInvestigate Sudden Metric Changes and Design A/B Test
Investigate Sudden Metric Changes and Design A/B Test evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - LyftMediumData ScientistInvestigate a 7% Monthly Active Riders Drop and a 20% Wait-Time Increase
Tests your ability to conduct a structured root-cause analysis on simultaneous marketplace metric regressions, decomposing supply-demand dynamics and separating correlation from causation.
Analytics & ExperimentationTechnical ScreenMarketplace AnalyticsRoot Cause AnalysisMetrics & KPIs - LyftMediumSoftware EngineerMinimum Window Substring with Repeated Required Characters
Tests the sliding window technique and frequency counting to find the shortest substring containing all required characters with their exact multiplicities in linear time.
Coding & AlgorithmsOnsiteSliding WindowTwo PointersHash Table - LyftMediumData ScientistOptimize Driver Repositioning for Minimal Pickup Time
This question evaluates spatial algorithms and real-time optimization for ride dispatch together with interval scheduling and capacity planning competencies, covering skills in computational geometry, routing, demand forecasting, and temporal overlap reasoning.
Coding & AlgorithmsOnsite - LyftMediumData ScientistQuery and transform marketplace data in SQL/Python
This question evaluates mastery of data manipulation and analytics skills in the Data Manipulation (SQL/Python) domain, including SQL aggregation and window functions, time-based joins and grouping, deduplication of status streams, handling late-arriving events, and implementing rolling metrics using both SQL and pandas.
Data Manipulation (SQL/Python)Onsite - LyftMediumMachine Learning EngineerReason About an Intercept Shift in Logistic Regression
Tests understanding of how an additive shift to the intercept of a logistic regression model impacts predicted probabilities, ranking metrics (ROC-AUC), thresholded classification metrics (Recall, FPR, Precision), and calibration.
Machine LearningTechnical ScreenLogistic RegressionModel EvaluationClassification Metrics - LyftEasySoftware EngineerReconstruct a BST from Preorder Traversal
Reconstruct the exact binary search tree represented by a preorder traversal of unique integers. Handle empty and completely skewed inputs at scale while preserving the intended tree rather than balancing it.
Coding & AlgorithmsOnsite - LyftMediumSoftware EngineerRotting Oranges
Compute how many simultaneous spread steps are needed to rot every reachable orange using multi-source grid traversal.
Coding & AlgorithmsOnsite - LyftMediumSoftware EngineerSmallest Covering Window in a String
Tests using the two-pointer sliding window pattern combined with frequency counting to find the shortest substring satisfying multiset inclusion constraints in linear time.
Coding & AlgorithmsTechnical Screensliding-windowtwo-pointershash-table - LyftMediumSoftware EngineerSolve grid compromise spread with BFS
This question evaluates proficiency in graph traversal and grid-based simulation, specifically multi-source breadth-first search, state propagation modeling, and algorithmic complexity analysis within the Coding & Algorithms domain, emphasizing practical application of implementation and reasoning.
Coding & AlgorithmsOnsite - LyftMediumSoftware EngineerSolve substring and worker assignment
This pair of problems evaluates competencies in string processing and multiplicity handling as well as scheduling and resource-allocation for interval tasks, testing the ability to choose and apply appropriate data structures while managing time and space complexity.
Coding & AlgorithmsOnsite - LyftMediumMachine Learning EngineerTrain vs Test Error Diagnosis, Overfitting Fixes by Model Type, and Cross-Validation
A machine learning fundamentals screen that asks you to diagnose models from pairs of training and test error, including the unusual case where the test error is the lower one. It continues with curbing overfitting in linear, tree-based and neural network models, and with setting up validation splits and k-fold cross-validation.
Machine LearningTechnical Screen - LyftMediumSoftware EngineerWrapper Over a Paginated Page API That Fetches the Next N Words
A coding question that wraps an external paginated API, where each page returns a variable number of words, behind a fetch(n) method that returns the next n words in order. It tests handling words left over between calls, requests that span several or empty pages, lazy fetching, the end of the content, and API failures.
Software Engineering FundamentalsOnsite
Lyft