OpenAI Coding Interview Questions

The OpenAI coding interview questions are known for being both intellectually challenging and deeply practical. They go beyond algorithmic puzzles, focusing on engineering maturity, clarity of thought, and scalability. OpenAI’s engineers work on large-scale AI infrastructure and cutting-edge systems, so the interview process is designed to find candidates who can think clearly, code efficiently, and reason about complex trade-offs.

This guide offers an interview roadmap by breaking down the OpenAI coding interview questions in a step-by-step format. You’ll learn what to expect, how to prepare, and how to solve common problems with explanations and working code.

Understanding the OpenAI Coding Interview

The OpenAI coding interview questions test both your problem-solving process and your engineering intuition. Interviewers evaluate not just the correctness of your answer, but also:

Clarity: How well you explain your thought process.
Efficiency: The time and space complexity of your solution.
Scalability: How your approach adapts to larger systems.
Communication: Whether you collaborate effectively and reason aloud.

Unlike generic coding tests, OpenAI interviews often start with standard algorithmic problems but evolve into design discussions, e.g., how you’d scale your code for production or handle real-world data.

Format of the Coding Interview

The OpenAI coding interview questions typically appear in one or two rounds:

Online or live coding round: Solve one or two medium-to-hard problems in 45–60 minutes.
On-site or virtual interview: Multiple coding sessions (data structures, algorithms, or small system problems) plus a behavioral and design component.

You can use your preferred programming language (Python, C++, or Java are common). Interviewers care less about syntax and more about the clarity of problem-solving.

Common Topics in OpenAI Coding Interviews

To prepare effectively, focus on these areas that repeatedly appear in OpenAI coding interview questions:

Arrays and strings (searching, sorting, manipulation).
Hash maps and sets (frequency counts, lookups).
Trees and graphs (BFS/DFS, shortest path, connected components).
Dynamic programming (memoization, tabulation).
System-scale algorithms (LRU cache, rate limiter, distributed counters).
Real-world logic questions that blend algorithms and design.

Sample Question 1: Weighted Random Selection

Question

Given a dictionary of key–weight pairs, implement pick_key() that returns a key with probability proportional to its weight.

Solution

import random

import bisect

class WeightedRandom:

    def __init__(self, weights):

        self.keys = list(weights.keys())

        self.prefix_sums = []

        total = 0

        for w in weights.values():

            total += w

            self.prefix_sums.append(total)

        self.total = total

    def pick_key(self):

        r = random.uniform(0, self.total)

        idx = bisect.bisect_left(self.prefix_sums, r)

        return self.keys[idx]

Explanation

Time complexity: \( O(log n) \) for binary search.
Space: \( O(n) \) for prefix array.
The function picks keys proportionally to weight, ensuring fairness across all selections.

Follow-Up

If weights change frequently, use a Binary Indexed Tree for \( O(log n) \) updates and queries—a typical scalability extension asked during OpenAI coding interview questions.

Sample Question 2: LRU Cache Implementation

Question

Design and implement an LRU (Least Recently Used) cache.

Solution

from collections import OrderedDict

class LRUCache:

    def __init__(self, capacity: int):

        self.cache = OrderedDict()

        self.capacity = capacity

    def get(self, key: int) -> int:

        if key not in self.cache:

            return -1

        self.cache.move_to_end(key)

        return self.cache[key]

    def put(self, key: int, value: int) -> None:

        if key in self.cache:

            self.cache.move_to_end(key)

        self.cache[key] = value

        if len(self.cache) > self.capacity:

            self.cache.popitem(last=False)

Explanation

This structure maintains recent usage order automatically. Both get and put run in \( O(1) \). It’s one of the most classic OpenAI coding interview questions because it tests real-world engineering logic.

Follow-Up

Interviewers may ask how to scale this for distributed caching (e.g., consistent hashing, Redis clusters).

Sample Question 3: Matrix Rotation

Question

Rotate a given NxN matrix 90° clockwise, in place.

Solution

def rotate_matrix(matrix):

    n = len(matrix)

    for i in range(n):

        for j in range(i, n):

            matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]

    for row in matrix:

        row.reverse()

    return matrix

Explanation

Transpose + reverse achieves rotation with \( O(1) \) extra space. This question checks your understanding of in-place transformations—a common variant in OpenAI coding interview questions that measure memory awareness.

Sample Question 4: Merge K Sorted Lists

Question

Merge k sorted linked lists into one sorted list.

Solution

import heapq

class Node:

    def __init__(self, val=0, next=None):

        self.val = val

        self.next = next

def merge_k_lists(lists):

    heap = []

    for i, node in enumerate(lists):

        if node:

            heapq.heappush(heap, (node.val, i, node))

    dummy = Node()

    curr = dummy

    while heap:

        val, i, node = heapq.heappop(heap)

        curr.next = node

        curr = curr.next

        if node.next:

            heapq.heappush(heap, (node.next.val, i, node.next))

    return dummy.next

Explanation

Uses a min-heap for efficient merging.
Time complexity: \( O(N log k) \).
Space: \( O(k) \).

You’ll find this or its variants frequently in OpenAI coding interview questions, as it tests real-world merging logic like log aggregation or stream handling.

Sample Question 5: Find Median from Data Stream

Question

Design a data structure that supports:

addNum(int num) — add a number to the data stream.
findMedian() — return the median of all numbers so far.

Solution

import heapq

class MedianFinder:

    def __init__(self):

        self.low, self.high = [], []  # max heap (invert sign), min heap

    def addNum(self, num):

        heapq.heappush(self.low, -num)

        heapq.heappush(self.high, -heapq.heappop(self.low))

        if len(self.high) > len(self.low):

            heapq.heappush(self.low, -heapq.heappop(self.high))

    def findMedian(self):

        if len(self.low) > len(self.high):

            return -self.low[0]

        return (-self.low[0] + self.high[0]) / 2

Explanation

Two heaps maintain order:

Left heap (low) stores smaller half (max-heap).
Right heap (high) stores larger half (min-heap).
Median comes from the roots.

A brilliant example of data balancing logic common in OpenAI coding interview questions.

Sample Question 6: Top K Frequent Elements

Question

Given a list of numbers, return the k most frequent elements.

Solution

import heapq

from collections import Counter

def topKFrequent(nums, k):

    count = Counter(nums)

    return [x for x, _ in heapq.nlargest(k, count.items(), key=lambda x: x[1])]

Explanation

Count frequencies using a hash map.
Use a heap for top-k extraction in \( O(n log k) \).

A simple yet powerful question from OpenAI coding interview questions that tests map/heap understanding.

Sample Question 7: Longest Substring Without Repeating Characters

Question

Find the length of the longest substring without repeating characters.

Solution

def lengthOfLongestSubstring(s):

    seen = {}

    left = 0

    max_len = 0

    for right, char in enumerate(s):

        if char in seen and seen[char] >= left:

            left = seen[char] + 1

        seen[char] = right

        max_len = max(max_len, right – left + 1)

    return max_len

Explanation

Sliding window with two pointers.
Efficient \( O(n) \) solution.

Tests state tracking and map logic—both key in OpenAI coding interview questions for string-heavy tasks.

Sample Question 8: Valid Parentheses

Question

Check if a string containing brackets ()[]{} is valid.

Solution

def isValid(s):

    stack = []

    pairs = {‘)’: ‘(‘, ‘]’: ‘[‘, ‘}’: ‘{‘}

    for c in s:

        if c in pairs.values():

            stack.append(c)

        elif not stack or stack.pop() != pairs[c]:

            return False

    return not stack

Explanation

This \( O(n) \) stack-based problem tests logic, matching, and data structure usage—a common feature in OpenAI coding interview questions that assesses implementation detail and accuracy.

Sample Question 9: Kth Largest Element in an Array

Question

Find the Kth largest number in an unsorted array.

Solution

import heapq

def findKthLargest(nums, k):

    return heapq.nlargest(k, nums)[-1]

Explanation

Using heaps provides \( O(n log k) \) time. You can also use Quickselect for \( O(n) \) expected time — an optimization that interviewers may ask about.

Sample Question 10: Two Sum Problem

Question

Given an array and a target, find the indices of two numbers that add up to the target.

Solution

def twoSum(nums, target):

    seen = {}

    for i, num in enumerate(nums):

        diff = target – num

        if diff in seen:

            return [seen[diff], i]

        seen[num] = i

Explanation

HashMap stores previously seen values.
\( O(n) \) time, \( O(n) \) space.

The Two Sum pattern frequently appears in OpenAI coding interview questions as a warm-up or screen.

Behavioral Layer in Coding Questions

Even during technical sessions, OpenAI interviewers pay attention to how you communicate:

Do you verbalize your approach?
Do you explain trade-offs clearly?
Do you ask clarifying questions before coding?
Do you handle mistakes gracefully?

These behavioral factors influence your evaluation almost as much as correctness. The OpenAI coding interview questions aim to reveal your overall engineering mindset, not just your syntax fluency.

Preparation Strategies

Here’s how to systematically prepare for the OpenAI coding interview questions:

Master fundamentals: Arrays, graphs, recursion, and dynamic programming.
Focus on clarity: Practice talking through your reasoning.
Simulate interviews: Use mock interview platforms or record yourself coding.
Study scalability: Be ready to extend your solution to large systems.
Rehearse follow-ups: Each solution should have an “if scaled up” story.
Use Educative or similar platforms: Courses like “Grokking the Coding Interview” and “Grokking the System Design Interview” provide excellent pattern coverage.

A good goal is to solve at least 100 coding problems across categories and conduct 3–5 mock interviews before your actual OpenAI session.

Final Tips

To succeed with OpenAI coding interview questions:

Think like an engineer, not a student. Focus on building working, scalable, readable code.
Explain continuously. Treat the session as collaborative problem-solving.
Anticipate follow-ups. Discuss time/space complexity and potential scaling issues.
Show curiosity. Ask about how similar challenges are handled in OpenAI’s real systems.
Stay calm. If you get stuck, verbalize your fallback plan—it often earns you credit.

Conclusion

The OpenAI coding interview questions are designed to identify engineers who combine algorithmic excellence with creativity, pragmatism, and communication. By practicing structured problem-solving, reviewing key algorithms, and understanding the rationale behind each question type, you can walk into your interview confident and prepared.

Remember: OpenAI isn’t just hiring coders—it’s hiring builders of the future of AI. Your code should demonstrate not only correctness but also clarity, responsibility, and scalability. If you can show that mindset in your answers, you’ll stand out.

Good luck, you’ve got this.

OpenAI Coding Interview Questions

Understanding the OpenAI Coding Interview

Format of the Coding Interview

Common Topics in OpenAI Coding Interviews

Sample Question 1: Weighted Random Selection

Question

Solution

Explanation

Follow-Up

Sample Question 2: LRU Cache Implementation

Question

Solution

Explanation

Follow-Up

Sample Question 3: Matrix Rotation

Question

Solution

Explanation

Sample Question 4: Merge K Sorted Lists

Question

Solution

Explanation

Sample Question 5: Find Median from Data Stream

Question

Solution

Explanation

Sample Question 6: Top K Frequent Elements

Question

Solution

Explanation

Sample Question 7: Longest Substring Without Repeating Characters

Question

Solution

Explanation

Sample Question 8: Valid Parentheses

Question

Solution

Explanation

Sample Question 9: Kth Largest Element in an Array

Question

Solution

Explanation

Sample Question 10: Two Sum Problem

Question

Solution

Explanation

Behavioral Layer in Coding Questions

Preparation Strategies

Final Tips

Conclusion

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