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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
- Topics
- 8
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Showing 2791–2820 of 4616 results
- NetflixMediumSoftware EngineerImplement weighted-eviction cache
This question evaluates understanding and practical implementation of data structures and algorithms for a weighted cache, including weighted eviction policies, use of ordered maps, and time-space complexity analysis.
Coding & AlgorithmsTechnical Screen - NetflixHardMachine Learning EngineerL1 vs L2 Normalization: Definitions, Geometry and When to Use Each
A machine learning fundamentals question asking how L1 normalization differs from L2 normalization of a vector. It tests precise definitions, what each normalized vector represents geometrically, when each is the right choice, and the ability to keep normalization distinct from L1 and L2 regularization.
Machine LearningTechnical Screen - NetflixMediumSoftware EngineerMovie Playlist With Live Score Updates and No Repeats per Loop, Plus Thread Safety
Implement an in-memory movie playlist in which get returns the highest-scoring movie not yet served in the current loop, starting a new loop once every movie has been served, while scores can change at any time. A follow-up asks where to add locks when many threads call it concurrently.
Software Engineering FundamentalsTechnical Screen - NetflixMediumSoftware EngineerPlan Eviction and Cleanup for a Production Cache
This problem tests the design of an in-memory caching system, focusing on memory management, eviction strategies, and concurrency control to ensure high performance and stability.
Software Engineering FundamentalsTechnical ScreenSystem DesignConcurrencyCaching - NetflixMediumSoftware EngineerReturn the longest contiguous subarray with all distinct values
This problem tests your ability to use the sliding window pattern and a hash map to maintain a range of unique elements efficiently in O(n) time.
Coding & AlgorithmsTechnical ScreenSliding WindowHash MapArrays - NetflixHardMachine Learning EngineerReview a Feature Pipeline PR for Production Risks, Then Serve Features by Freshness
Review a feature pipeline pull request whose tests all pass and explain what could break once it runs in production, from time leakage and training-serving skew to data quality and scale. Then decide how the same features should be served when the model needs day-old, minute-fresh or request-time values.
ML System DesignTechnical Screen - NetflixMediumSoftware EngineerSession Token Cache With Per-Entry Expiry and a Test Suite
Implement an in-memory cache whose entries expire individually, reclaim memory for keys that are never read again, and prove it with deterministic tests using an injected clock. Tests expiry semantics, stale-entry handling and test design.
Software Engineering FundamentalsTechnical Screendata-structuresheapcaching - NetflixMediumSoftware EngineerSimulate a TTL Cache with LRU Eviction
Implement a fixed-capacity key-value cache combining per-key TTL expiration with LRU eviction and O(1) average time per operation.
Coding & AlgorithmsTechnical ScreenCacheLRUHash Table - NetflixMediumSoftware EngineerSolve non-repeating show substring
This question evaluates understanding of sequence processing, uniqueness detection, and the use of auxiliary data structures to track previously seen elements when finding non-repeating contiguous subsequences, applied to show names instead of characters.
Coding & AlgorithmsTechnical Screen - NetflixMediumSoftware EngineerSolve sliding-window and disjoint-string-pairs tasks
This question tests the ability to combine sliding window techniques with bit manipulation or frequency counting to identify pairs of substrings that share no common characters.
Coding & AlgorithmsTechnical ScreenStringsBit ManipulationSliding Window - NetflixMediumMachine Learning EngineerTransform flat keys into nested dictionary
This question evaluates proficiency in parsing and restructuring hierarchical parameter keys into nested dictionary structures, string manipulation for grouping, handling nonconforming keys and duplicates, and reasoning about time and space complexity.
Data Manipulation (SQL/Python)Technical Screen - NewrelicHardSoftware EngineerMaximum Path Sum in an Indexed Binary Tree
This problem tests tree traversal techniques on an index-based representation, focusing on handling negative weights and memory efficiency via an iterative post-order approach.
Coding & AlgorithmsTechnical ScreenBinary TreeDynamic ProgrammingIterative Traversal - NewrelicHardSoftware EngineerProduct of Array Except Self Without Division
This problem tests array manipulation skills and the ability to optimize space complexity by using prefix and suffix product passes instead of division.
Coding & AlgorithmsTechnical ScreenArraysPrefix-SumOptimization - NewsBreakMediumMachine Learning EngineerDerive and implement calibration via temperature scaling
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Derive and implement calibration via temperature scaling states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - NewsBreakHardMachine Learning EngineerDesign approach for class imbalance
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design approach for class imbalance states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - NewsBreakMediumSoftware EngineerFind Optimal Piecewise Constant Regression Parameters
This problem tests the ability to derive optimal constants for a piecewise regression model with a single split point and to implement an efficient search algorithm to minimize the sum of squared errors.
Machine LearningTechnical ScreenRegressionOptimizationPrefix Sums - NewsBreakMediumMachine Learning EngineerWalk through a key project
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Walk through a key project states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - NexonMediumProduct ManagerHandle Partner Pushback
Answer a game PM behavioral question about handling cross-functional pushback from design, engineering, analytics, publishing, and monetization partners. The solution uses STAR structure, active listening, shared goals, player data, MVP options, guardrails, and decision clarity.
Behavioral & LeadershipTechnical Screen - NexonMediumProduct ManagerInvestigate Item Usage Spike
Investigate an in-game item usage spike by verifying instrumentation, segmenting players and item sources, building hypotheses across patches, economy shifts, promotions, exploits, and bots, then choosing actions with retention, economy, monetization, and trust guardrails.
Product / Decision MakingTechnical Screen - NextdoorMediumMachine Learning EngineerAnswer HR motivation and project experience questions
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Answer HR motivation and project experience questions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - NextdoorMediumMachine Learning EngineerBuild Ranked Feed With Photo Batching
This question evaluates algorithmic reasoning and data manipulation skills, including stable sorting, grouping/batching, and merging of ranked items while preserving ordering invariants.
Coding & AlgorithmsTechnical Screen - NextdoorMediumMachine Learning EngineerBuild a model using only pandas/numpy
This question evaluates proficiency in applied machine learning competencies including data preprocessing, categorical encoding, feature scaling, implementing baseline models and optimization using numpy/pandas, and selecting appropriate evaluation metrics.
Machine LearningTechnical Screen - NextdoorMediumMachine Learning EngineerBuild an end-to-end ML classification pipeline
This question evaluates a candidate's competency in building end-to-end tabular classification pipelines, including data loading and splitting, missing-value handling, categorical encoding, feature scaling, model training and comparison, hyperparameter tuning, metric-based evaluation, model persistence, and batch inference.
ML System DesignTechnical Screen - NextdoorMediumMachine Learning EngineerDescribe resume highlights and confirm logistics
Describe resume highlights and confirm logistics evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - NextdoorMediumSoftware EngineerDesign In-Memory Transactions with Snapshots and Savepoints
Tests your ability to design an in-memory key-value store supporting nested transactions, named savepoints, and reliable rollback semantics using undo logs or snapshotting.
Software Engineering FundamentalsTechnical ScreenData StructuresSystem FundamentalsDesign - NextdoorHardData EngineerDesign KPI dashboard tables: retention, weekly rollups, timezone
This Nextdoor data engineer system design screen asks you to design the analytical data model and incremental ETL behind a fast, large-scale KPI dashboard for a photo-sharing app. It covers derived aggregate table design (grain, keys, partitioning), DAU/new-user/new-photo and follower-distribution metrics, 7-day deltas, then extends to week-1 cohort retention, weekly rollups, and switching the reporting timezone. The focus is correctness, performance at scale, and maintainability with timezone handled as a reporting parameter.
System DesignTechnical Screen - NextdoorMediumSoftware EngineerDesign a Job Scheduler That Can Scale by 100 Times
Design a distributed, asynchronous job scheduler that starts at modest traffic but can seamlessly scale up 100x. Evaluates API design, data storage trade-offs, worker coordination, task deduplication, and failure handling across scale boundaries.
System DesignTechnical ScreenDistributed SystemsJob SchedulerScalability - NextdoorMediumSoftware EngineerDesign a Neighborhood Newsfeed System
Design a privacy-aware, hyperlocal social feed that serves real-time updates and emergency alerts based on strict neighborhood boundaries.
System DesignTechnical ScreenFeed ArchitectureGeospatialDistributed Systems - NextdoorMediumSoftware EngineerDesign a Scalable Job Scheduler
This question evaluates system design skills focused on job scheduling, distributed systems concerns (state modeling, retries, idempotency), API and data-model design, fault tolerance, observability, and scalability within the System Design domain.
System DesignTechnical Screen - NextdoorEasyiOS EngineerFlatten Nested Comments
This problem tests your ability to traverse a nested tree structure (comments) and flatten it into a linear representation while respecting hierarchical depth levels.
Coding & AlgorithmsTechnical ScreenTreesRecursionData Structures
Netflix
Nextdoor