Jane Street interview questions

Showing 1–30 of 47 results

• Alternating Die-Roll Game: Optimal Accept/Reject StrategyHard

  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.

  Jane StreetQuantitative TraderSoftware Engineering FundamentalsTechnical Screen
• Analyze trading RFQ competitiveness dataMedium

  This question tests your ability to model order flow competitiveness by deriving features from trade logs and assessing the relationship between pricing aggressiveness and win probability.

  Jane StreetQuantitative TraderMachine LearningOnline Assessment
• Board Simulation: Bottom-Insert Pieces with Uniform Row and Column DetectionMedium

  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.

  Jane StreetMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Bottom-Insertion Connect Game: Detect the First k-in-a-Row WinnerMedium

  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.

  Jane StreetSoftware EngineerCoding & AlgorithmsTechnical Screen
• Bounds on the Probability of Rain on at Least One Weekend DayEasy

  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.

  Jane StreetQuantitative TraderMachine LearningTechnical Screen
• Build a Web Calculator That Matches a Reference AppMedium

  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.

  Jane StreetFrontend EngineerSoftware Engineering FundamentalsTechnical Screen
• Build a time-series forecasting modelHard

  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.

  Jane StreetMachine Learning EngineerMachine LearningTechnical Screen
• Calculate win chance in three-player dice gameMedium

  This question evaluates understanding of probability theory, conditional probability, and modelling of repeated stochastic events such as ties and rerolls, and is categorized under Statistics & Math.

  Jane StreetSoftware EngineerStatistics & MathHR Screen
• Code Editor with Block Shrink and Expand (Code Folding)Medium

  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.

  Jane StreetMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Connect-N on an Infinite Board with GravityMedium

  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.

  Jane StreetMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Decode an Encrypted Paragraph TreeHard

  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.

  Jane StreetSoftware EngineerCoding & AlgorithmsTechnical Screen
• Design Exchange and Instrument Abstractions for Quote ArbitrageMedium

  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.

  Jane StreetSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design a Real-vs-Fake DNA ClassifierMedium

  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.

  Jane StreetQuantitative ResearcherMachine LearningTechnical Screen
• Design an end-to-end training frameworkHard

  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.

  Jane StreetMachine Learning EngineerML System DesignTechnical Screen
• Design and implement a tiny language runtimeHard

  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.

  Jane StreetMachine Learning EngineerSystem DesignTechnical Screen
• Design exchange–trading system message flowEasy

  This question evaluates a candidate's knowledge of distributed messaging protocols, fault-tolerant system design, and financial trading interface competencies, including defining message contracts, session management, and guarantees like idempotency and ordering.

  Jane StreetSoftware EngineerSystem DesignOnsite
• Dice Duel: 30-Sided Die vs. 20-Sided Die with One Blind RerollMedium

  This question evaluates probabilistic reasoning, expected-value comparison, and decision-making under uncertainty in discrete random processes. It is commonly asked to test a candidate's ability to reason about optimal stochastic decisions and trade-offs in blind reroll scenarios, classified under probability and decision theory within the Machine Learning domain and assessing conceptual understanding rather than practical implementation.

  Jane StreetQuantitative TraderMachine LearningOnsite
• Estimate the Number of Florists in Sydney and Make a MarketMedium

  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.

  Jane StreetQuantitative TraderStatistics & MathTechnical Screen
• Evaluate Expressions with Strictly Positive Integer Intermediate ResultsMedium

  Evaluate arithmetic expressions with a supplied decomposition helper, rejecting nonpositive or fractional intermediate results and preserving exact integer division.

  Jane StreetSoftware EngineerCoding & AlgorithmsTechnical Screen
• Field Layout: getOffset and insertMedium

  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.

  Jane StreetFrontend EngineerCoding & AlgorithmsTechnical Screen
• Five Pumpkins Weighed in Every Pair: Find the Total WeightMedium

  This question evaluates combinatorial counting, algebraic reasoning, and mental-arithmetic skills by testing understanding of pairwise sums and how individual elements contribute to aggregate totals.

  Jane StreetQuantitative ResearcherSoftware Engineering FundamentalsOnsite
• Four Fair Coins: Expected Payoff and Pricing Two Magic WandsEasy

  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.

  Jane StreetQuantitative TraderMachine LearningTechnical Screen
• Implement Connect Four with bottom push-upMedium

  This question evaluates proficiency in data structures, algorithm design, state management, and API design for game mechanics, focusing on board representation, move operations, win detection, and undo/redo functionality.

  Jane StreetSoftware EngineerCoding & AlgorithmsOnline Assessment
• Implement a Circular BufferMedium

  Implement a fixed-capacity circular buffer (ring buffer) supporting constant-time FIFO operations using wraparound indexing.

  Jane StreetSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement compiler for custom languageHard

  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.

  Jane StreetMachine Learning EngineerSystem DesignTechnical Screen
• Marketplace Data Store: Order Matching and End-of-Day ReconciliationEasy

  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.

  Jane StreetMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Minimum Common Pairwise Correlation Among Seven Identically Distributed Random VariablesMedium

  This question evaluates understanding of correlation constraints, covariance/correlation matrix structure, and spectral analysis, combining probability, statistics, and linear algebra competencies within the Machine Learning/Data Science domain.

  Jane StreetQuantitative ResearcherMachine LearningOnsite
• Optimal Stopping for Die Rolls That Bust on Perfect SquaresMedium

  This question evaluates probabilistic reasoning and optimal stopping competency, testing understanding of Markov decision processes, expected-value calculations, and risk trade-offs in stochastic processes.

  Jane StreetQuantitative TraderCoding & AlgorithmsOnsite
• Optimal Stopping with Square-Number RuinHard

  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.

  Jane StreetSoftware EngineerStatistics & MathTechnical Screen
• Optimize a Two-Box Place-or-Take GameHard

  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.

  Jane StreetSoftware EngineerStatistics & MathTechnical Screen