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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 2101–2130 of 4616 results
- Jain GlobalMediumData EngineerDesign and Debug a Point-in-Time Backtest Data Pipeline
This problem tests the ability to architect a robust financial data pipeline that ensures point-in-time accuracy and reproducible research.
System DesignTechnical ScreenData EngineeringFinancial DataBacktesting - Jain GlobalMediumData EngineerModel Effective-Dated Compliance Restrictions for Securities
This question tests the ability to design a time-series database schema capable of handling bitemporal data, identifier changes, and overlapping compliance restrictions.
System DesignTechnical ScreenData ModelingTime-SeriesBitemporal - Jane StreetHardQuantitative TraderAlternating Die-Roll Game: Optimal Accept/Reject Strategy
This question evaluates probabilistic reasoning, dynamic decision-making and zero-sum game analysis, testing competency in expected-value calculations, optimal stopping and symmetric strategic reasoning.
Software Engineering FundamentalsTechnical Screen - Jane StreetMediumMachine Learning EngineerBoard Simulation: Bottom-Insert Pieces with Uniform Row and Column Detection
This question evaluates the ability to simulate a 2-D game board and maintain dynamic state invariants, testing array manipulation, state-tracking, and detection of uniform rows and columns under bottom-insert semantics.
Coding & AlgorithmsTechnical Screen - Jane StreetMediumSoftware EngineerBottom-Insertion Connect Game: Detect the First k-in-a-Row Winner
This question evaluates simulation and algorithmic problem-solving skills, focusing on state maintenance for an unbounded grid, efficient detection of k-in-a-row runs in rows and columns, and handling column-wise shifts that can produce simultaneous winners.
Coding & AlgorithmsTechnical Screen - Jane StreetEasyQuantitative TraderBounds on the Probability of Rain on at Least One Weekend Day
This question evaluates understanding of probability theory and dependence between events, specifically the ability to reason about bounds on the probability of a union given fixed marginals, which is a core competency for probabilistic modeling in Machine Learning and Data Scientist roles.
Machine LearningTechnical Screen - Jane StreetMediumFrontend EngineerBuild a Web Calculator That Matches a Reference App
This question evaluates frontend engineering competencies—interactive UI implementation, state management, event-driven logic, and visual layout fidelity—within the domain of software engineering fundamentals and web frontend development.
Software Engineering FundamentalsTechnical Screen - Jane StreetHardMachine Learning EngineerBuild a time-series forecasting model
Build a time-series forecasting model evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - Jane StreetMediumMachine Learning EngineerCode Editor with Block Shrink and Expand (Code Folding)
This question evaluates a candidate's ability to model and manipulate indentation-based hierarchical structures, maintain nested visibility state for collapsed blocks, and map between original file lines and dynamic display numbering.
Coding & AlgorithmsTechnical Screen - Jane StreetMediumMachine Learning EngineerConnect-N on an Infinite Board with Gravity
This question evaluates implementation skills for efficient algorithms and data structures to manage a sparse, unbounded grid with gravity and to detect N-in-a-row runs both vertically and horizontally.
Coding & AlgorithmsTechnical Screen - Jane StreetHardSoftware EngineerDecode an Encrypted Paragraph Tree
This question evaluates skills in string parsing, tree serialization/deserialization, and bijective substitution cipher handling, assessing competency in parsing nested structures, character mapping, and traversal within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - Jane StreetMediumSoftware EngineerDesign Exchange and Instrument Abstractions for Quote Arbitrage
Design an in-memory cross-exchange quote aggregator and arbitrage detector that identifies profitable, actionable price discrepancies while handling invalid spreads, quote staleness, and order sizing.
Software Engineering FundamentalsTechnical ScreenLow-Level DesignTrading SystemsData Structures - Jane StreetMediumQuantitative ResearcherDesign a Real-vs-Fake DNA Classifier
A Jane Street Data Scientist technical-screen question on building a classifier that distinguishes real from fake DNA sequences, given a small labeled real/fake set plus a large real-only corpus. It tests sequence representation (k-mers, embeddings, CNN/Transformer), hybrid supervised + anomaly-detection framing, realistic hard-negative generation, evaluation under class imbalance, leakage prevention, threshold calibration, and biological failure modes like reverse-complement symmetry and GC-content shortcuts.
Machine LearningTechnical Screen - Jane StreetHardMachine Learning EngineerDesign an end-to-end training framework
Design an end-to-end training framework evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - Jane StreetHardMachine Learning EngineerDesign and implement a tiny language runtime
Design and implement a tiny language runtime evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - Jane StreetMediumQuantitative TraderEstimate the Number of Florists in Sydney and Make a Market
Tests Fermi estimation, sanity checking via dual perspectives (demand vs. supply/geographic), quoting a two-sided market under uncertainty, and updating beliefs given market flow or new information.
Statistics & MathTechnical ScreenFermi EstimationMarket MakingProbability - Jane StreetMediumSoftware EngineerEvaluate Expressions with Strictly Positive Integer Intermediate Results
Evaluate arithmetic expressions with a supplied decomposition helper, rejecting nonpositive or fractional intermediate results and preserving exact integer division.
Coding & AlgorithmsTechnical Screen - Jane StreetMediumFrontend EngineerField Layout: getOffset and insert
This question evaluates a candidate's ability to model and maintain ordered field layouts by computing and updating byte offsets, testing competencies in dynamic sequence data structures, cumulative offset tracking, and efficient update/query operations.
Coding & AlgorithmsTechnical Screen - Jane StreetEasyQuantitative TraderFour Fair Coins: Expected Payoff and Pricing Two Magic Wands
The question evaluates probabilistic reasoning, expected-value computation, and invariant-based state-space analysis as applied to adaptive operations on random variables and pricing contingent instruments.
Machine LearningTechnical Screen - Jane StreetMediumSoftware EngineerImplement a Circular Buffer
Implement a fixed-capacity circular buffer (ring buffer) supporting constant-time FIFO operations using wraparound indexing.
Coding & AlgorithmsTechnical ScreenData StructuresQueueArray - Jane StreetHardMachine Learning EngineerImplement compiler for custom language
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Implement compiler for custom language states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - Jane StreetEasyMachine Learning EngineerMarketplace Data Store: Order Matching and End-of-Day Reconciliation
This question evaluates understanding of stateful data structures, deterministic order-matching logic, and time-ordered reconciliation of transaction logs, focusing on correct handling of pricing, tie-breaking, and re-opened inventory.
Coding & AlgorithmsTechnical Screen - Jane StreetHardSoftware EngineerOptimal Stopping with Square-Number Ruin
Analyze an optimal stopping game where a player rolls a fair six-sided die to accumulate points but busts if the running sum hits a positive perfect square. Tests dynamic programming, renewal theory intuition, and rigorous edge-case analysis under uncertain stopping boundaries.
Statistics & MathTechnical Screenprobabilityoptimal-stoppingdynamic-programming - Jane StreetHardSoftware EngineerOptimize a Two-Box Place-or-Take Game
Analyze and optimize a 100-turn, two-box stochastic game with capacity constraints and opaque states. Derive the optimal decision policy, examine boundary/edge cases, and evaluate how the strategy changes when core assumptions are relaxed.
Statistics & MathTechnical ScreenProbabilityDynamic ProgrammingMarkov Decision Process - Jane StreetMediumSoftware EngineerPayout-Split Formula Where Every Step Is a Whole Positive Share
Write a parser and evaluator for arithmetic formulas that describe splitting a prize pool into whole-unit shares, where every intermediate computed amount must be a positive whole number or the entire formula is rejected. Tests recursive-descent parsing combined with evaluation rules where a single bad step invalidates the whole result.
Coding & AlgorithmsTechnical Screenparsingrecursionarithmetic - Jane StreetHardMachine Learning EngineerPredict future time-series values
This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Predict future time-series values states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - Jane StreetMediumQuantitative TraderPricing a Mystery Money Box: Expected Value, Adverse Selection, and Competition
This question evaluates probabilistic reasoning and economic-incentives competencies, including expected-value calculation, risk-neutral decision-making, adverse selection, and surplus division in bargaining contexts.
Machine LearningTechnical Screen - Jane StreetMediumSoftware EngineerProcess One Million Quote Updates per Second
Design a low-latency market data processing engine capable of ingesting 1,000,000 quote updates per second across multiple exchanges, maintaining global best bids and asks, and triggering immediate cross-venue arbitrage alerts.
System DesignTechnical ScreenLow LatencyConcurrencyTrading Systems - Jane StreetHardSoftware EngineerReach a Target Using Each Number At Most Once
This question evaluates the ability to construct arithmetic expressions from a multiset of numbers and reason about combinatorial search, operator composition, parenthesization, and exact rational arithmetic under constraints.
Coding & AlgorithmsTechnical Screen - Jane StreetMediumQuantitative TraderSealed-Bid Auction for a Box of 200 Coin Flips (and an Informed Opponent)
This question evaluates understanding of probability and expected-value reasoning, auction and game-theoretic strategic thinking, Bayesian inference under asymmetric information, and quantitative decision-making relevant to a Machine Learning-focused data scientist role.
Machine LearningTechnical Screen
Jane Street