OpenAI Coding Interview Questions: Patterns and Top Problems
OpenAI’s tagged question list is short, and that is the most useful fact on this page. LeetCode currently carries fewer than twenty problems tagged to the company. The list also breaks the usual pattern of big tech banks. Most entries are build-a-small-system problems rather than pure algorithm puzzles, and Mediums dominate almost completely.
This page shows the complete list, ranked by frequency score, with a link from every row to the pattern guide on this site that teaches its core technique. Because the list is small, you can genuinely finish it. The study plan below takes advantage of that.
The short version
- All seventeen tagged problems fit on one study list. Full coverage is the goal here.
- Fourteen of seventeen rank Medium, about 82%. Expect build-style Mediums over puzzles.
- Design and arrays each touch 11 problems, about 65%. Hash tables follow at 8 problems.
What Is Publicly Known About OpenAI’s Loop
Public reports describe OpenAI’s process in less detail than the larger companies’, so treat this section as a rough map.
- A recruiter conversation covers your background and role fit.
- Technical screens follow with live coding.
- Later rounds mix coding with discussion of past work and system choices.
- Interviewers tend to probe how you reason about practical engineering, not only whether code runs.
- Some roles add domain specific rounds tied to the team.
The thin documentation makes the tagged problem list more valuable than usual. When process details are scarce, the questions themselves carry the signal, and the signal here says practical construction over puzzle solving.
Every Question Tagged to OpenAI
LeetCode assigns each company-tagged problem a relative frequency score from 0 to 100 based on candidate reports. The table lists all seventeen problems currently tagged to OpenAI. Seven reach the very top band. Scores under 50 get their own lower band here, because cutting them would hide half of a very small dataset.
If you learn five guides first, learn these
- Start with array manipulation . Eleven of the seventeen problems carry that tag.
- Pair it with hash table . Design SQL and Authentication Manager both store keyed state.
- Learn string manipulation next. Encode and Decode Strings and IP to CIDR both sit in the top band.
- Add stack for the iterator and parser rows. Three table rows use it.
- Finish with binary search . Time Based Key-Value Store is the model problem.
| Problem | Difficulty | Demand | Pattern Guide |
|---|---|---|---|
| Design Memory Allocator | Medium | Very high | Array Manipulation |
| Design SQL | Medium | Very high | Hash Table |
| Encode and Decode Strings | Medium | Very high | String Manipulation |
| Flatten Nested List Iterator | Medium | Very high | Stack |
| Rotting Oranges | Medium | Very high | Graph Traversal |
| Time Based Key-Value Store | Medium | Very high | Binary Search |
| IP to CIDR | Medium | Very high | String Manipulation |
| Simplify Path | Medium | High | Stack |
| Design Excel Sum Formula | Hard | High | Topological Sort |
| Largest Local Values in a Matrix | Easy | Lower | Matrix |
| Asteroid Collision | Medium | Lower | Stack |
| Trapping Rain Water | Hard | Lower | Two Pointers |
| Design Spreadsheet | Medium | Lower | Array Manipulation |
| Design Hit Counter | Medium | Lower | Queue |
| Design Authentication Manager | Medium | Lower | Hash Table |
| Snapshot Array | Medium | Lower | Array Manipulation |
| LRU Cache | Medium | Lower | Linked List |
Look at what the top seven share. Six of them ask you to define data structures and make operations fast or correct inside a small simulated system. Even Encode and Decode Strings is really a protocol design task. This matches how modern infrastructure teams work, and it explains why practicing only classic algorithm puzzles covers this list poorly.
Topics and Difficulty at OpenAI
The topic counts come from all tagged OpenAI problems. The share is the percent of those problems carrying the topic, and shares add past 100 percent because topics overlap.
| Topic | Tagged Problems | Share |
|---|---|---|
| Design | 11 | ~65% |
| Array Manipulation | 11 | ~65% |
| Hash Table | 8 | ~47% |
| String Manipulation | 7 | ~41% |
| Stack | 4 | ~24% |
| Matrix | 4 | ~24% |
| Binary Search | 3 | ~18% |
Design ties arrays at the top, which appears nowhere else on this site’s company pages. At Google, Meta, and Amazon, design tags sit near 4 percent. Apple comes closest among large companies at about 16 percent, and this list triples that. Hash tables follow right behind, since most of these builds store and look up state constantly.
Difficulty barely spreads:
| Difficulty | Problems | Share |
|---|---|---|
Easy | 1 | 6% |
Medium | 14 | 82% |
Hard | 2 | 12% |
Four of five tagged problems are Mediums, the heaviest Medium lean of any company page here. One Easy exists in the whole list. Prepare at Medium depth and expect the interview to start there.
Method: every problem currently carrying the OpenAI tag on LeetCode, ranked by relative frequency scores from 0 to 100. Shares overlap because one problem can carry several topics.
Where to Spend Your Prep Time
- Practice building before optimizing. Pick one design problem per week and write the full class interface first, then fill in behavior. Memory Allocator and Authentication Manager are good starts.
- Practice hash map patterns until they are automatic. Nearly half the list stores keyed state. The hash table guide covers the core forms.
- Learn the stack tricks behind iterators and parsers. Flatten Nested List Iterator and Simplify Path both live there. The stack guide teaches both shapes.
- Add binary search over time for timestamped storage. Time Based Key-Value Store is the model problem. The binary search guide has the base form.
- Review on a fixed schedule until interview day. A seventeen-problem list is finishable, but only if nothing fades. Spaced repetition handles the timing for you.
graph TD
A["Design-first builds"] --> B["Array and hash table state"]
B --> C["String parsing"]
C --> D["Stack shapes and binary search over time"]
D --> E["All seventeen solved once"]
E --> F["Spaced repetition until interview day"]
The diagram walks the prep plan from system building down to full review coverage.
Questions Candidates Ask
Can seventeen problems cover an OpenAI loop?
The table holds all seventeen tagged problems, with seven rows in the very top band. Solve each one once. Keep the set warm with reviews.
Why does design show up everywhere?
Design tags touch 11 problems, about 65%. Most rows ask you to build a small system with fast operations. Write the class interface before behavior.
Where should I start?
Open the hash table guide first since nearly half the list stores keyed state. Follow it with the stack guide for the iterator and parser rows.
Next Steps
Solve all seventeen problems once, then let reviews handle the second and third pass. The linked guides above cover every technique involved, and the coding patterns index lists the full library. For comparison shopping across offers, see how much wider the pools run in the Google and Meta guides.