Katkat

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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
Companies
11
Topics
8
Languages

Showing 6421–6450 of 9403 results

  • OpenAIHardSoftware Engineer
    Reason About Binary Search with One-Call-Delayed Feedback

    Implement and analyze binary search under a 1-step delayed-feedback protocol where each query response evaluates the previous guess.

    Software Engineering FundamentalsTechnical Screenbinary-searchinteractivealgorithms
  • OpenAIMediumSoftware Engineer
    Rebalance shard ranges under overlap limit

    This problem tests interval manipulation and greedy optimization by requiring you to adjust a set of shard ranges so that no more than k ranges overlap at any integer point, while minimizing the number of boundaries moved.

    Coding & AlgorithmsTechnical ScreenAlgorithmsSweep-LineIntervals
  • OpenAIMediumSoftware Engineer
    Recruiter Screen: Current Work, What You Want Next, Level and Work Authorization

    Prepare for a recruiter screen for a software engineering role that asks what you are working on now, what you want next along with your current level and promotion history, and your work authorization. Tests concise, concrete answers that give a recruiter what they need to move a candidate forward.

    Behavioral & LeadershipHR Screen
  • OpenAIMediumSoftware Engineer
    Refactor a Chatbot into Clean Object-Oriented Components

    Tests object-oriented design and refactoring skills by decoupling a monolithic chatbot script into modular, testable, and extensible components adhering to SOLID principles.

    Software Engineering FundamentalsTechnical ScreenObject-Oriented DesignRefactoringDesign Patterns
  • OpenAIHardSoftware Engineer
    Refactor a chat message processing function

    This problem tests your ability to refactor procedural, error-prone code into a robust, extensible pipeline for parsing and managing chat message state.

    Coding & AlgorithmsTechnical ScreenRefactoringString-ManipulationOOP
  • OpenAIHardMachine Learning Engineer
    Repair a Broken Tiny Language Model, Then Make Decoding Incremental

    Find four planted bugs in a compact PyTorch decoder-only language model (some produce NaNs, some silently ruin learning), then add key/value caching so generation processes one new token per step.

    Machine LearningOnsitepytorchtransformersdebugging
  • OpenAIHardSoftware Engineer
    Resolve Short-Link Redirect Chains That May Loop

    Follow single-target redirects from a start code and return the final code, or the last code before the walk would revisit one. Then answer many starts in linear total time. Tests visited-set bookkeeping and functional-graph structure.

    Coding & AlgorithmsTechnical Screengraphsfunctional-graphcycle-detection
  • OpenAIMediumSoftware Engineer
    Return the Lexicographically Smallest Topological Order

    Implement a deterministic topological sort using a min-heap to return the lexicographically smallest ordering of a directed graph, or detect cycles and return an empty list.

    Coding & AlgorithmsOnsitegraphtopological-sortheap
  • OpenAIHardSoftware Engineer
    Rotate Couriers Daily at Minimum Total Cost

    Choose one courier company per day so that no company works two days in a row and the total price is minimal, then return the schedule. Tests DP over a small state, path reconstruction, and the best/second-best trick for k options.

    Coding & AlgorithmsTechnical Screendynamic-programmingreconstructionarrays
  • OpenAIHardMachine Learning Engineer
    Schedule Incremental Labeling Tasks

    This problem tests the ability to design an incremental task scheduler that balances workload across a pool of annotators while maintaining fairness and deterministic state transitions.

    Coding & AlgorithmsOnsiteHeapsSchedulingGreedy
  • OpenAIHardMachine Learning Engineer
    Search Monster Battle Strategies

    Design a search algorithm using state-space exploration to find the minimum number of turns required to defeat a boss given transient health, scripted boss attacks, and a single-use revive.

    Coding & AlgorithmsOnsiteBreadth-First SearchState Space SearchGraph Algorithms
  • OpenAIHardMachine Learning Engineer
    Select high-quality math documents from crawls

    This question assesses the ability to design a scalable pipeline that ingests, cleans, extracts, and filters high-quality mathematical content from diverse, noisy web-crawled sources.

    ML System DesignOnsiteSystem DesignNLPData Engineering
  • OpenAIMediumSoftware Engineer
    Serialize and Restore an In-Memory Key-Value Store

    Design and implement a deterministic binary serializer and strict deserializer for a string key-value store, ensuring robust input validation against corruption, truncation, duplicate keys, and trailing data.

    Software Engineering FundamentalsTechnical Screenbinary-serializationsystemsdata-encoding
  • OpenAIHardMachine Learning Engineer
    Simulate Infection Spread on a Grid

    Implement a discrete-time cellular automaton simulating epidemic spread on a 2D grid with multi-day infection states, immunity waning, and mortality rules.

    Coding & AlgorithmsTechnical ScreenSimulationMatrixCellular Automata
  • OpenAIMediumMachine Learning Engineer
    Simulate Infection Spread with Immunity and Recovery

    Implement a cellular automaton simulation modeling disease spread on a 2D grid with discrete cell states and timed recovery.

    Coding & AlgorithmsTechnical ScreenGrid SimulationCellular AutomataBFS
  • OpenAIHardMachine Learning Engineer
    Simulate Plant Infection With Controlled Burning

    This question evaluates a candidate's ability to implement discrete-time grid simulations with delayed state transitions, reason about simultaneous updates and neighborhood effects, and perform a limited combinatorial optimization (choosing one row or column) to minimize a global outcome.

    Coding & AlgorithmsOnsite
  • OpenAIMediumMachine Learning Engineer
    Simulate Turn-Based Monster Battles

    This problem tests your ability to design a stateful simulation engine that handles turn-based mechanics, complex status modifiers, and dynamic combat interactions.

    Coding & AlgorithmsOnsiteSimulationState ManagementGame Logic
  • OpenAIEasySoftware Engineer
    Simulate a Turn-Based Two-Player Game

    This problem tests your ability to design a robust state management system and implement validation logic for a rule-based game.

    Coding & AlgorithmsTechnical ScreenState ManagementAPI DesignSimulation
  • OpenAIMediumSoftware Engineer
    Sliding-Window Chat Analytics: Recent Counts, Live Sessions, Out-of-Order Events

    A three-part coding exercise: build an in-memory chat analytics service that counts each chat's interactions in a closed 15-minute sliding window, tracks each user's live sessions from touch and close events, and stays correct when events arrive out of order. It tests bounded-memory eviction, efficient updates and queries, and exact window and ordering semantics.

    Software Engineering FundamentalsOnsite
  • OpenAIMediumSoftware Engineer
    Sliding-Window Forklift Telemetry: Pings, Shifts, Late Events

    Build an in-memory telemetry service that counts a forklift's recent pings in a 15-minute window, tracks which forklifts are currently on an active shift, and stays correct when pings arrive late and out of order. Tests bounded-memory eviction and exact window semantics.

    Software Engineering FundamentalsOnsitesliding-windowstreamingdata-structures
  • OpenAIMediumSoftware Engineer
    Social Graph with Follow/Unfollow, Snapshots, History Queries and Recommendations

    A software engineer phone-screen coding problem: build an in-memory social graph that supports follow and unfollow, point-in-time snapshots, queries of who followed whom as of an earlier snapshot, and recommendations of users to follow. It tests data-structure choice, versioned history, and deterministic ranking.

    Software Engineering FundamentalsTechnical Screen
  • OpenAIMediumSoftware Engineer
    Spreading Contagion on a Grid

    Tests modeling multi-source breadth-first search (BFS) on a 2D grid to compute simultaneous state propagation across discrete time steps.

    Coding & AlgorithmsTechnical Screenmulti-source-bfsgraphsmatrix
  • OpenAIMediumSoftware Engineer
    Spreadsheet engine with setCell/getCell, cell references, and cycle detection

    Build a small spreadsheet engine with setCell and getCell, where formulas reference other cells and a write that would create a circular dependency must be detected. The exercise also asks for live unit tests, probing data modeling, dependency graphs, cache invalidation and test design.

    Software Engineering FundamentalsOnsite
  • OpenAIHardMachine Learning Engineer
    Streaming Entropy with Numerical Stability

    Implement an online, numerically stable algorithm to compute Shannon entropy over an unbounded stream of categorical tokens with O(1) updates.

    Coding & AlgorithmsTechnical Screenstreamingmathnumerical-computing
  • OpenAIHardSoftware Engineer
    Text Editor in Four Stages: Buffer, Undo/Redo, Trie Autocomplete, Collaborative OT

    Build a text editor in four stages: a buffer with insert, delete and read, undo and redo with two stacks, trie-based autocomplete ranked by word frequency, and real-time collaborative editing with operational transformation. It tests data structure choice, reversible operations, top-k retrieval and concurrency correctness.

    Software Engineering FundamentalsOnsite
  • OpenAIMediumSoftware Engineer
    Three-Card Game: Deal Hands, Rank Them, Then Add a Flush Category

    A two-part card game coding exercise: deal three cards to each player from one deck, show every hand and pick the winner using three of a kind, pair and high card, then add a flush category between them. It tests hand representation, within-category comparison, tie handling and code that extends cleanly when a rule changes.

    Software Engineering FundamentalsTechnical Screen
  • OpenAIHardSoftware Engineer
    Toy Language Types: Render Types and Signatures, Infer Generic Return Types

    Model the types of a toy programming language, with primitives, generic type variables and nested tuples, render types and function signatures as strings, then infer a call's concrete return type by binding generics consistently across arguments. It tests recursive data modeling, exact formatting and type inference with clear errors.

    Software Engineering FundamentalsOnsite
  • OpenAIMediumMachine Learning Engineer
    Track Expiring GPU Credits

    Design an in-memory credit ledger to track expiring GPU compute credits, support priority consumption, and manage resource debt.

    Coding & AlgorithmsOnsiteHeapDesignGreedy
  • OpenAIHardMachine Learning Engineer
    Train a classifier and analyze dataset

    This question evaluates proficiency in end-to-end supervised machine learning workflows—covering data validation, exploratory data analysis, preprocessing, model training and selection, evaluation metrics and calibration, fairness assessments, and reproducibility—and tests competencies in applied machine learning engineering, statistical reasoning, and model governance within the Machine Learning domain. It is commonly asked in technical interviews because it verifies practical implementation skills and judgment for deploying reliable classifiers, aligning metrics to business goals and diagnosing performance and fairness across slices, with emphasis on practical application informed by conceptual understanding.

    Machine LearningTechnical Screen
  • OpenAIMediumMachine Learning Engineer
    Train and analyze a classifier

    This question evaluates proficiency in end-to-end machine learning engineering, including exploratory data analysis, time-aware train/validation/test splitting, model baseline and improvement, class imbalance strategies, leakage-safe hyperparameter tuning, metric computation and calibration, error analysis, reproducible training pipelines with CLI/config/seed control, explainability (feature importance/SHAP and ablations), and documentation of risks, fairness checks and monitoring hooks. It is commonly asked to assess practical ability to manage the full ML lifecycle and data hygiene in production-like scenarios, testing applied Data Manipulation (SQL/Python) and Machine Learning competencies with an emphasis on practical application while also requiring conceptual understanding of evaluation, calibration and fairness.

    Data Manipulation (SQL/Python)Technical Screen