OpenAI Coding Interview (SWE): Actual Past Questions Asked & their Unique Question Style

Estimated read time: 10–15 minutes

Summary: OpenAI SWE coding interviews are often work-like rather than classic LeetCode-style puzzles. Candidates report practical prompts, lots of follow-ups, and a strong bar for clean, correct, production-ready code under time pressure. This guide breaks down the question styles, what’s being evaluated, and how to prepare for the follow-up-heavy format.

TL;DR + FAQ (read this first)

At-a-glance takeaways

• Expect 45 to 60 minutes and problems that can feel large in scope
• Prompts are often work-like, not contrived brainteasers
• You should expect a lot of follow-ups. These can happen midway as you solve, which can throw some people off their train of thought. Be ready for this
• Especially common with experienced candidates are refactoring/code review-style problems

Quick FAQ

Is the OpenAI coding interview LeetCode-style?
Not typically. Classic DS&A still matters, but questions are often more practical (debugging, refactoring, concurrency, practical APIs).

How long are coding interviews at OpenAI?
Commonly around 45 to 60 minutes. Time pressure is real, and problems can be multi-part.

What matters more: speed or code quality?
Both. Candidates report a high bar for doing a lot quickly, but interviewers also care about correctness, clean structure, edge cases, and how you validate your solution.

Do I need to write tests during the interview?
You’re not always required to, but quick sanity tests will be viewed positively, especially in debugging/refactoring-style tasks.

What’s the biggest “gotcha”?
The follow-up chain. The sheer amount of coding you may need to do for some problems can feel overwhelming, especially for concurrency-related problems.

Can I choose my preferred programming language?
Often yes.

1) What the OpenAI SWE coding interview is really testing

You’re being tested on something broader than “can you solve it.”

What they’re really evaluating

1) Problem-solving under uncertainty
Prompts can be multi-step and occasionally “long and hard to understand.” The test includes how you gain clarity, not just how you code.

2) Time management + prioritisation
You often need to implement a core solution quickly, while also thinking about edge cases and performance as the interviewer adds constraints.

3) Engineering fundamentals, not trivia
Classic DS&A still matters, but it’s commonly embedded in more practical scenarios.

4) Communication as a technical skill
They watch how you justify decisions, make/validate assumptions, and respond to feedback or requirement changes.

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2) Past OpenAI coding interview questions

Below are actual questions real candidates have faced, grouped by category. For each, we've included the problem statement as posed, what it actually entails, and what interviewers are really evaluating.

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A) State + interfaces (work-like utilities)

Actual question: "Implement a resumable iterator for a large dataset."

What this actually entails:

• Design an iterator that can pause mid-traversal and resume later from the exact position
• Handle the case where the underlying dataset is too large to fit in memory
• Define a clean interface (e.g., next(), pause(), resume(), position())
• Consider serialization of state if the iterator needs to persist across sessions

The hidden complexity:

• What happens if the dataset changes between pause and resume?
• How do you handle edge cases like resuming at the end of the dataset?
• Memory efficiency: are you streaming or buffering?

What they're testing:

• Can you define a crisp, usable interface quickly?
• Can you maintain state safely across operations?
• Can you discuss memory constraints (streaming vs loading everything)?
• Do you think about failure modes and edge cases proactively?

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B) Real-world parsing + edge cases

Actual question: "Implement a function to normalize filesystem paths, resolving . and .. components and handling symbolic links."

Example input/output:

Input:  "/home/user/docs/../photos/./img.png"
Output: "/home/user/photos/img.png"

With symlinks: {"/photos": "/media/external/photos"}
Input:  "/photos/../docs"
Output: ? (depends on symlink resolution order)

What this actually entails:

• Use a stack to process path segments
• Handle . (current directory) by ignoring it
• Handle .. (parent directory) by popping from the stack
• Resolve symbolic links using a provided mapping dictionary
• Return the canonical absolute path

The hidden complexity:

• Root handling: what does /../ resolve to? (Answer: /)
• Redundant slashes: /home//user///docs should normalize
• Symlink cycles: what if /a → /b and /b → /a?
• Order of operations: do you resolve symlinks before or after ..?
• What does "canonical" even mean in this context?

What they're testing:

• Stack-based algorithm implementation
• Systematic handling of edge cases
• Asking clarifying questions about ambiguous requirements
• Clean string manipulation and state management

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C) Versioning / time-based data structures

Actual question: "Design a time-versioned data store. Implement a data structure that stores key-value pairs with timestamps and can return the value for a given key at a given time."

Required API:

store.put(key, value)           # stores with current timestamp
store.get(key)                  # returns latest value
store.get(key, timestamp)       # returns value as of that timestamp
store.get(key, version=3)       # returns value at version 3

What this actually entails:

• Design the internal data structure (hash map of keys → list of (timestamp, value) pairs)
• Implement efficient lookup for "as of" queries (binary search on timestamps)
• Handle versioning semantics (what if no value existed at that time?)

The hidden complexity:

• Should timestamps be auto-generated or caller-provided?
• What's the memory strategy for many versions of the same key?
• How do you handle get(key, timestamp) when timestamp is before the first write?
• Concurrent writes with the same timestamp?

What they're testing:

• Data structure design decisions
• Time/space complexity awareness
• Binary search implementation (for efficient historical lookups)
• API design sensibility

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D) Concurrency fundamentals

Actual question: "What is a race condition? Identify the race condition in this code and modify it to prevent the issue."

You might see code like:

class Counter:
    def __init__(self):
        self.count = 0

    def increment(self):
        current = self.count
        # Context switch could happen here
        self.count = current + 1

What this actually entails:

• Explain race conditions conceptually (two threads accessing shared state, outcome depends on timing)
• Identify the specific vulnerability (read-modify-write is not atomic)
• Fix using appropriate primitives (locks, atomics, thread-safe queues)
• Discuss how you'd verify the fix is correct

The hidden complexity:

• Locks vs atomics: when to use which?
• Deadlock potential when adding locks
• Performance implications of different synchronization strategies
• How do you test concurrent code?

What they're testing:

• Fundamental understanding of threading hazards
• Knowledge of coordination primitives (locks, mutexes, semaphores, atomics)
• Practical debugging skills for concurrent code
• Ability to reason about correctness under concurrency

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E) Concurrent worker patterns (queues, pools, shared state)

Actual question: "Implement a web crawler. The crawler should fetch pages starting from a URL, do so concurrently if possible, and avoid visiting duplicate URLs."

Requirements typically include:

• Start from a seed URL
• Extract links from each page and add to a queue
• Crawl concurrently up to N workers
• Never visit the same URL twice
• Respect some termination condition (max pages, max depth, or time limit)

Skeleton you might build toward:

class WebCrawler:
    def __init__(self, max_workers=10):
        self.visited = set()
        self.queue = Queue()
        self.lock = Lock()

    def crawl(self, seed_url):
        # Your implementation
        pass

    def worker(self):
        # Fetch URL, extract links, add new ones to queue
        pass

The hidden complexity:

• Thread-safe access to the visited set
• Graceful shutdown when the queue is empty but workers are still running
• Error handling for failed fetches (timeouts, 404s, etc.)
• Rate limiting to avoid overwhelming servers
• What if extracted links are relative URLs?

What they're testing:

• Thread pool / worker pattern implementation
• Safe coordination of shared state (visited set, queue)
• Practical concurrent programming (not just theory)
• Language-specific concurrency features (goroutines, Python threading, etc.)

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F) Debugging / refactoring tasks

Actual question: "Given this block of code, debug it, improve its performance, and refactor for clarity without changing its behavior."

You might receive:

• A Python function that parses log files but runs O(n²) when it should be O(n)
• Code with subtle bugs (off-by-one, incorrect boundary conditions)
• Poorly named variables and deeply nested logic
• Missing error handling that would crash on edge inputs

What this actually entails:

• Read and comprehend unfamiliar code quickly
• Identify correctness issues first (bugs, race conditions, edge cases)
• Then address performance (algorithmic complexity, hot spots)
• Then refactor for clarity (naming, structure, decomposition)
• Validate changes with test cases

The hidden complexity:

• You're modifying code you didn't write under time pressure
• Must preserve behavior while improving implementation
• Interviewers watch how you approach understanding the code
• Writing quick tests to verify your changes is expected

What they're testing:

• Code reading speed and comprehension
• Debugging methodology (do you reason systematically or guess?)
• Refactoring judgment (what's worth changing?)
• Validation discipline (do you prove your fix works?)

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G) Mini system implementation

Actual question: "Build a simple ORM (Object-Relational Mapping) layer. Define classes and methods to save, query, and update objects in a database."

What this actually entails:

• Design a base Model class that other classes inherit from
• Implement save(), find(), update(), delete() methods
• Map Python/JS objects to database rows
• Handle type conversions and basic query building

Example interaction:

class User(Model):
    table = "users"
    fields = ["id", "name", "email"]

user = User(name="Alice", email="[email protected]")
user.save()  # INSERT INTO users ...

found = User.find(id=1)  # SELECT * FROM users WHERE id = 1
found.name = "Alicia"
found.update()  # UPDATE users SET name = 'Alicia' WHERE id = 1

What they're testing:

• OOP design and abstraction skills
• Understanding of database operations
• API design sensibility
• Going beyond algorithm trivia to real software patterns

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3) The follow-up chain: how it typically unfolds

A recurring pattern reported by candidates is getting lots of follow-ups. This can happen midway through your solution (which can put some people off), or after you've “completed” your implementation.

Expect follow-ups like:

• “What failure mode did you ignore?”
• “Can you refactor this to be cleaner?”
• “How would you test this?”
• “What if inputs are empty/huge/malformed?”

You're going to need a lot of mental stamina. This is not the kind of interview to go into sleep-deprived or burnt out, because you're going to be pushed hard.

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4) Code quality signals that move the needle

OpenAI-style coding interviews reward code that looks like it belongs in a real codebase.

Signals that read as “strong”:

• Clear interfaces and function boundaries
• Explicit handling of edge cases (or at least calling them out)
• Reasonable performance (and awareness of bottlenecks)
• Basic validation/testing mindset
• Readable structure over clever tricks
• Thoughtful error handling where relevant

Pro Tip: If you’re short on time, favour readability and correctness over micro-optimisations.

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5) A note on refactoring/code review rounds

For more experienced hires, a common pattern is being given code and asked to improve it.

What “good” looks like:

• Identify correctness risks first (bugs, race conditions, edge cases)
• Then address performance (big-O and hot spots)
• Then refactor for clarity (naming, structure)
• At the very least, outline the test cases that you would run through. Dry-run them if you can