OpenAI Coding Interview Questions

Implement a queue that lends each task out for a fixed window, takes it back when the holder goes quiet, and gives up on it after a set number of attempts. This OpenAI round is pure state bookkeeping against a logical clock: reservations, expiry, retry ordering, and the dead-letter destination all have to agree. You will decide what an expired lease does to queue position, keep a stale token from completing work twice, and spend attempts in the right place. The complete problem and a reference implementation are available to subscribers.

Design a resilient persistence layer with custom serialization rules in this compelling challenge inspired by OpenAI technical interviews. The exercise tests your ability to encode arbitrary data structures containing complex characters directly into binary formats without relying on standard built-in serializers, while also handling strict file size constraints. Gain immediate access to the complete problem description and model code by subscribing.

Build a work queue that hands out tasks under a lease, retries the ones that fail, and refuses to release anything whose prerequisites have not finished. This OpenAI question combines dependency ordering with the bookkeeping of a real queue: reservation tokens, expiring leases, attempt budgets, and a dead-letter destination. You will decide when an expired lease returns work to the ready set, keep tokens from being replayed, and release dependents only on genuine success. The complete problem and reference implementation require a subscription.

Encountered in software engineering interviews at OpenAI, this hard-level design exercise challenges applicants to manage a resource ledger involving timed allocations and sequential deductions. Candidates must efficiently track expiration timelines, update balances dynamically, and optimize data retrieval using advanced heap and greedy strategies. To view the comprehensive problem statement and reference implementation, a paid subscription is necessary.

A coding question on simulating a turn-based fight between two ordered teams of creatures and recording every event. It tests careful rule-following, clean class design, and exact handling of ties and edge cases.

Featured in recent OpenAI technical screens, this simulation challenge tasks you with modeling the spread of contagion across a multidimensional cellular field. You will need to implement state transition logic governed by specific temporal thresholds, neighborhood dynamics, and immunity factors. The exercise tests your ability to translate complex biological or epidemic rules into clean, scalable matrix operations within strict time limits. The full problem and model solution require a subscription.

A coding and design question on in-memory graph state. You build a follow network that can freeze point-in-time copies, answer relationship queries on them, and suggest new connections, then reason about scale, concurrency and testing.

Solve a distributed systems puzzle focusing on dynamic range allocation and shard rebalancing, highlighted in recent engineering interviews at OpenAI. Your task involves maintaining a collection of keyed intervals, handling insertions and deletions, and executing a rebalance routine to cap maximum overlaps while minimizing data movement. This problem challenges your algorithmic thinking regarding interval sorting, gap filling, and efficient resource partitioning. Access the full problem details and expert solution architecture by subscribing today.

Compute the statistical entropy of a softmax probability distribution while avoiding catastrophic numerical underflow or overflow in this algorithmic challenge reported from OpenAI. You will first implement a stable batch calculation using logarithmic shifting techniques and then extend your solution to handle a streaming data architecture where inputs arrive sequentially and metrics update in an online fashion. Access the complete problem specification, mathematical breakdown, and model solution with a subscription.

Build iterators that can be paused, serialised, and resumed from exactly where they stopped, first over a flat list and then over two- and three-dimensional data. This OpenAI round is a test-driven design exercise: the position itself becomes a value you have to define, store, and validate. You will decide what a resume token contains, keep nested dimensions consistent, and defend the interface against a token from a different dataset. The full problem and the reference design require a paid subscription.

Featured in OpenAI software engineering interviews, this distributed systems challenge revolves around analyzing a machine cluster arranged in a tree hierarchy. You must design recursive or message-passing algorithms to compute the total node count and map out the entire network topology using only local neighbor communication. This problem tests your mastery of graph traversal, tree properties, and distributed coordination. To read the complete problem breakdown, architectural considerations, and model solutions, a subscription is required.

Discover facts about a cluster of machines arranged as a tree, where each machine can only pass messages to its immediate parent and children. Asked at OpenAI, the difficulty is that sends are asynchronous, delivery order is not guaranteed, and messages can arrive more than once. You will design a request-and-aggregate protocol over that unreliable channel, make handlers idempotent, and decide how a node knows its subtree has finished reporting. A subscription unlocks the full problem and the complete protocol design.

Featured in OpenAI interview rounds, this systems programming problem requires engineering an efficient memory allocation and deallocation manager. Candidates must avoid naive linear scans and design a data structure that handles fixed-capacity resource allocation and release operations with optimal time complexity. The task tests deep understanding of memory management algorithms, pointer arithmetic, and data structure selection for performance-critical applications. Reviewing the full problem requirements and complete model solution necessitates a subscription.

Dive into deep learning internals by implementing custom automatic differentiation and parallel scan algorithms, as seen in technical evaluations at OpenAI. You will compute inclusive prefix products and construct manual backward passes for matrix sequences before optimizing the workflow using log-depth parallel techniques. This task evaluates your understanding of gradient propagation, tensor operations, and parallel computing paradigms. Unlock the complete problem requirements and the optimal model solution through our paid subscription.

Examine a sophisticated probability and stochastic reasoning challenge frequently asked in OpenAI technical interviews, focusing on optimizing randomized algorithms through strategic timeout designs. This exercise evaluates your mastery of mathematical bounds, expected value calculations, and restart strategies to minimize completion times for probabilistic tasks under uncertainty. It tests theoretical reasoning rather than raw programming skill. The complete analytical breakdown and expert solutions require a paid subscription.

Designed around a complex scheduling scenario reported at OpenAI, this coding task requires building a fair allocation engine for multi-party workflows involving models, humans, and specific tasks. You will implement robust distribution logic that satisfies strict quota limits, workload balancing thresholds, and uniqueness constraints across various participation phases. This problem thoroughly tests advanced greedy allocation, state management, and constraint satisfaction techniques. Access to the full problem text and complete model solution requires a subscription.

A coding question on implementing a small type checker for a toy language with primitive, generic, tuple and function types. It tests recursive data structures, pretty-printing, and resolving a call's return type by matching types consistently.

Master high-performance matrix operations and spatial classification techniques inspired by coding evaluations at OpenAI. This exercise challenges practitioners to implement distance-based prediction logic entirely through vectorized array manipulations, avoiding standard iteration loops, before translating the identical procedure into a neural network forward propagation framework. Learn how to leverage advanced mathematical libraries for optimal efficiency by accessing the full problem breakdown and expert solution.

Design a robust network address iterator capable of parsing IP ranges and CIDR blocks while supporting bi-directional traversal and membership checks, featured in interviews at OpenAI. The problem unfolds incrementally through multiple implementation stages, testing your ability to handle boundary conditions, numerical conversions, and iterator protocols cleanly. It evaluates your knack for writing modular, self-consistent code that scales with evolving requirements. Read the full multi-part problem description and examine the clean solution code with a subscription.

Determine the earliest compatible software version supporting a specific feature when dealing with complex, non-monotonic system behavior and hierarchical versioning strings. Reported as a challenging OpenAI interview question, this problem tests your algorithmic resourcefulness far beyond standard binary search by requiring custom adaptive strategies for erratic success criteria. You will need to optimize query counts against an unknown black-box function. Unlock the full problem details and professional implementation with a subscription.

This coding challenge, featured in OpenAI technical screenings, requires candidates to build a thread-safe traffic throttling utility that evaluates whether incoming calls should pass based on specific time-window rules. It tests concurrency management, state maintenance under high load, and algorithmic efficiency in a multithreaded environment. Access to the complete problem breakdown and verified solution code requires an active subscription.

In this reported OpenAI interview question, you are asked to design and implement an in-memory caching system featuring time-to-live expiration semantics. The exercise tests your ability to handle data retention rules, optimize retrieval performance, and manage eviction behavior efficiently. You will need to choose appropriate internal data structures to handle expiration thresholds and updates gracefully while maintaining optimal time complexity. Access to the full problem breakdown and the optimal model solution requires a subscription.

This OpenAI interview question asks you to construct a balanced sequence of content pairs subject to strict prefix constraints across multiple categories. The task evaluates your combinatorial reasoning, algorithmic thinking, and ability to maintain structural invariants dynamically. It is an excellent test of fine-grained sequence generation and constraint management. Unlock the complete problem specification and optimal algorithmic solution by purchasing a subscription.

This string simulation challenge, reported from OpenAI interviews, asks you to write a command-line utility that mimics a terminal navigation tool. You must correctly process both relative and absolute path modifications, handle parent directory jumps, and gracefully manage edge cases like malformed paths or going past the root directory. The exercise evaluates your command of string parsing, state tracking, and stack data structures. Gain full access to the problem requirements, test cases, and clean code solution with a subscription.

Explore advanced concurrency concepts with this Python synchronization problem frequently utilized in machine learning infrastructure interviews at OpenAI. You are tasked with implementing specialized locking primitives that manage thread access across distinct operational modes while preventing thread starvation through FIFO fairness guarantees. This exercise examines your mastery of low-level concurrency controls, condition variables, and thread coordination. Unlock the complete problem specifications and the reference model solution with a paid subscription.

This code reading evaluation from OpenAI requires candidates to analyze, refactor, and extend a substantial PyTorch codebase centered around noisy classifier architectures. Instead of building from scratch, engineers must quickly orient themselves within an unfamiliar project, evaluate time and space complexities of tensor operations, and implement structural enhancements. Success demonstrates strong codebase navigation and advanced deep learning framework proficiency. Explore the complete codebase analysis and model refactoring steps with a paid subscription.

Featured in OpenAI interview sessions, this coding problem requires engineering a path resolution utility that mimics standard directory navigation behavior, including relative routing and symbolic link handling. It evaluates string manipulation, tree traversal logic, and edge-case management in file system representations. The comprehensive problem description and optimal coding solution are restricted to subscribers.

In this coding interview question reported at OpenAI, you are asked to build a mini spreadsheet application that manages cell references, mathematical formulas, and automatic dependency updates. The challenge requires handling arithmetic operations while detecting and preventing circular references among cell links, with follow-ups exploring optimized evaluation graphs. It tests your graph traversal algorithms, state management, and object-oriented design capabilities. Access to the complete problem breakdown and verified model solution requires a subscription.

A coding question on aggregation over a tree of machines that can only message their parent and children. It tests whether a handler waits for every child's reply under arbitrary delivery order, rebuilds the tree's shape in a stable order, and spots why a subtree maximum that never recurses goes wrong.

Reported as an OpenAI coding challenge, this problem investigates how internet protocol addresses can be sequentially traversed and compressed into efficient routing blocks. You will work with bitwise representations, range aggregations, and scalable iteration patterns suitable for massive address spaces. The task tests your bit manipulation proficiency and ability to optimize network block calculations. The full problem and model solution require a subscription.

Reported during technical interviews at OpenAI, this coding task requires building an in-memory database that handles fundamental query processing operations directly through native method calls rather than parsing raw text strings. Developers must progressively implement core features such as table creation, record insertion, column projection, conditional filtering, and sorted output, followed by an architectural discussion on indexing optimizations. Unlock the full problem description, edge case analyses, and a complete model solution by purchasing a site subscription.

A design and coding question on a store that remembers every value of a key over time and answers lookups for any past moment, with a follow-up on concurrent access.

Work out the fewest continuous pen strokes needed to draw every line segment of a figure, where no segment may be retraced. This OpenAI question is a classic graph result in disguise: the answer falls out of vertex parities and connectivity, not from a search over drawing orders. You will count odd-degree vertices per connected component, handle components that are already closed circuits, and ignore isolated points that need no stroke at all. A subscription unlocks the full problem and the complete derivation.

In this engaging OpenAI interview challenge, you are asked to design a custom traversal mechanism that can be paused and later revived from its exact previous point. The problem assesses your object-oriented design skills and your ability to serialize and restore internal traversal states cleanly without breaking standard iteration contracts. It tests advanced software engineering principles and state management techniques. The full problem and model solution require a subscription.

A coding question on building an iterator wrapper whose progress can be checkpointed and restored, including after a restart. It tests a clear state contract, lazy reading of possibly infinite sources, and well-defined behaviour for invalid or out-of-range positions.

Read a tabular dataset off disk and estimate the probability of a target event using Bayesian reasoning rather than a library call. This OpenAI screening task is as much about engineering as statistics: the code has to load real files, run standalone, and come with its own tests at several data scales. You will decide how to handle unseen feature combinations, where smoothing belongs, and how to keep the estimate numerically sane on larger inputs. The complete problem and a reference solution require a paid subscription.

Audit a pile of crowd annotations to find the single worst human labeller, judged against the majority opinion on each item rather than against any ground truth. Reported at OpenAI, this exercise rewards clean aggregation in two passes and punishes anyone who forgets what happens when a vote ties. You will group annotations per item, resolve majorities, score each labeller by disagreement rate, and break ties deterministically. The full problem statement and the reference implementation are available with a subscription.

Write a working PyTorch training loop for a tiny two-layer network, and then find the planted mistakes in a version that trains but never learns. This OpenAI round tests everyday framework fluency: the gradient reset, the loss that expects logits rather than probabilities, and the shape mismatches that broadcast silently instead of raising. You will demonstrate that the loss genuinely falls and explain which line was responsible for it not falling before. The complete problem and the corrected reference loop require a subscription.

Aggregate a stream of chat events and report, for every event as it arrives, what the last five minutes of activity looked like. This OpenAI coding round is a sliding-window exercise dressed as product telemetry, and the interesting part is evicting stale events cheaply while preserving the original output order. You will pick between a deque and an index-based window, handle several event types at once, and keep the per-event cost constant. Subscribers can read the complete problem and the model solution.

Design a follow graph where every edge carries the moment it came into effect, so any query has to be answered as of a point in time. This OpenAI design-and-code round grows in stages: point-in-time lookups first, then reverse-direction queries, then friend-of-friend recommendations. You will choose a representation that keeps both directions queryable, decide where to binary search over time, and keep recommendation counting from degrading to a full scan. The full problem and reference code are behind a subscription.

Build a key-value store you can query as it existed at any past moment, with deletes, snapshots, and the ability to roll the whole store back. Asked at OpenAI, this problem is about choosing a versioned representation up front, because a design that only keeps current values cannot answer a historical read at all. You will handle tombstones for deleted keys, binary search within a key's history, and the semantics of restoring a snapshot while older versions stay addressable. A subscription unlocks the complete problem and the verified implementation.

Advance a grid of living and dead cells through several generations under the classic birth-and-death rules, where every cell updates at the same instant. This OpenAI warm-up looks trivial and catches people who mutate the board in place, because a half-updated grid feeds the wrong neighbour counts into the rest of the sweep. You will deal with edge and corner cells, the synchronous update requirement, and whether to buy a second buffer or encode both states in one. Subscribers get the full problem statement and a clean reference solution.

Count the connected friend groups in a social network given as a dense adjacency matrix, where friendship spreads transitively through mutual acquaintances. This OpenAI screen is a graph-traversal fundamental with a twist of representation: the input is a matrix, so the cost of visiting neighbours is different from an edge list. You will choose between depth-first search, breadth-first search, and union-find, and argue the complexity that follows from the matrix form. The full problem and model solution are available to subscribers.

Construct a sequence of daily tag pairs so that no prefix of the schedule ever drifts out of balance between the three tags involved. Posed at OpenAI, this constructive problem has a greedy answer that is easy to write and hard to justify, and the follow-up constraint rules out the first rule most candidates reach for. You will track running counts per tag, prove the invariant holds at every prefix length, and recognise the lengths for which no valid sequence exists. Subscribers can read the full problem and the constructive proof.

Resolve a set of package requirements into an installable plan, honouring version ranges and reporting an honest conflict when no plan exists. Reported at OpenAI, this engineering-heavy question grows through several stages and rewards correctness and clear data modelling over clever optimisation. You will model constraints as a graph, order work so that dependencies come first, and distinguish a genuine conflict from a resolution order you have not tried yet. A subscription gives access to the complete problem and the reference resolver.

Route a time-ordered stream of chat messages to whichever automated assistants a message mentions, and emit their replies in the right order. This OpenAI exercise is deliberately light on algorithms and heavy on specification reading: several assistants can fire from one message, and the ordering of their responses is part of the contract. You will parse mentions reliably, decide what happens when none or all of them match, and keep the output deterministic. Subscribers get the full problem statement and the model solution.

Schedule a daily stream of labelling work across human annotators and models, with per-day capacity limits and a history that nobody may repeat. This OpenAI problem is an assignment question in disguise, and the accumulating history is what stops yesterday's greedy answer from working today. You will enforce one task per person per day, respect the no-repeat rule across all previous rounds, and keep the allocation fair as the backlog shifts. The full problem statement and the model solution are available with a subscription.

Simulate an outbreak across a field of plants where infection spreads to the eight surrounding cells once a neighbour threshold is crossed, and some cells can never be infected. This multi-part OpenAI question starts with counting days to saturation and then adds variations that break any solution tangled up in its own update order. You will keep day boundaries strictly synchronous, detect when the spread has stalled instead of looping forever, and handle immune cells that split the grid. Subscribers get the complete problem and the verified simulation.

Implement a miniature query layer over records held in memory, supporting exact-match filters on several fields that combine like a conjunctive where clause. Asked at OpenAI, this question is about interface design as much as lookup speed: you choose the signatures, so the reviewer sees exactly how you think about extensibility. You will decide between scanning and indexing per field, compose filters without duplicating code, and say what happens when a filter names a field that does not exist. The full problem and reference engine require a subscription.

Find how long a contagion takes to reach every machine in a network when several machines are already compromised and infection spreads one hop per minute. Reported at OpenAI, this is a multi-source breadth-first search, and the giveaway mistake is running one search per seed instead of seeding the queue with all of them at once. You will push every infected node in as the starting layer, count layers rather than paths, and detect the unreachable case. Subscribers can read the complete problem and the model solution.

Count how many runs of consecutive integers can be chosen from one to n without ever including a forbidden pair of values together. This OpenAI counting problem is small enough to brute force and interesting because the forbidden pairs turn it into a constrained interval count rather than a formula. You will enumerate candidate ranges efficiently, decide where each forbidden pair truncates them, and avoid double counting overlapping restrictions. The full problem and the reference counting argument are available to subscribers.

Build an iterator that walks IPv4 addresses in dotted form, first upward through a range, then downward, then across a CIDR block. This OpenAI question is a bit-manipulation exercise wearing a string costume: the clean solution converts to a 32-bit integer once and formats only on output. You will handle inclusive endpoints, exhaustion at the top and bottom of the range, and the prefix arithmetic that turns a block into first and last addresses. The full problem and the reference iterator require a subscription.

Place one new interval into a sorted, non-overlapping list and merge whatever it touches, keeping the result ordered and disjoint. This OpenAI warm-up is short and unforgiving: the three regions before, across, and after the insertion each need their own handling, and an off-by-one at a shared endpoint changes the answer. You will decide whether touching intervals count as overlapping, and keep the pass linear instead of re-sorting. Subscribers get the complete problem and a clean reference solution.

Split a run of digits into every arrangement that forms a legal IPv4 address, in dictionary order, using each digit exactly once. This OpenAI exercise is backtracking with a narrow validity rule, and the trap is the leading zero: a lone zero segment is fine while anything padded is not. You will prune on segment value and length early, keep the output ordered, and report nothing at all when no split works. The full problem statement and the model solution require a subscription.