OpenAI L4 Interview Guides & Questions (2026)

OpenAI L4 Software Engineer Interview Guide

Preparing for coding, system design, and discussions of your past work

Updated Sep 6, 2026

OpenAI's L4 interview combines hands-on technical work with a close look at the projects you've built. A useful preparation benchmark is senior-level scope, roughly comparable to Google's L5. The interview process includes coding, system design, and discussions about the decisions you've made while shipping software with a team.

Below, we cover what each part of the interview involves and how to prepare, with advice from OpenAI Senior candidates. We also keep our interview question collection up to date with reports from users, so you can practice questions candidates have recently been asked. Your recruiter can fill in the scheduling details and any preparation specific to your loop.

What the process looks like

You'll usually start with a recruiter call, where you can talk through your background and learn more about the role. From there, you'll move into the technical assessments. OpenAI uses both live coding interviews and take-home projects, depending on the team. In the live-interview format, candidates describe coding and system-design screens before the final loop. The take-home format gives you an assignment to complete on your own, then discuss and extend with an engineer.

The final interviews bring together further technical work, discussions about projects you've built, and conversations about how you work with other people. Your recruiter can confirm which assessments your team uses and what you should prepare for each round. Get that outline early, since a take-home requires a different preparation schedule from a live screen.

Stage	What to expect	What to confirm
Initial conversation	Background, interests, and fit for the role	Target team and level
Technical assessments	Live coding and system design, or a take-home with a technical review	Assessment format, expected time commitment, environment, and allowed tools
Final interviews	Further technical work and conversations about your experience	Round list, project-presentation requirements, and schedule
Decision	Feedback from recruiting and any remaining steps	Expected response date and references

OpenAI's interview guide describes four to six hours of final interviews over one or two days, usually virtual, with a San Francisco onsite option. It aims to provide updates within a week after each stage and a decision within a week of the final interviews. References may be requested at that point. Share any competing offer deadline early.

The recruiter conversation

The first step in the loop is a chat with your recruiter. They'll want to hear about what you've worked on, what you're looking for next, and why you're interested in OpenAI. It's also your chance to learn more about the role and ask how the rest of the interview process will work.

It's worth reading about the team before this call. A useful answer to "Why OpenAI?" connects something you've built or want to work on to something the team actually does.

Ask what scope of work the team expects and whether you're interviewing for a particular team or a process that could lead to several teams. Senior and Staff comparisons vary across candidate accounts, so settle the responsibilities and target level together.

"They expect most people to downlevel coming in, regardless of what company you're from"

— OpenAI Senior candidate

That quote reflects one candidate's experience rather than an official downleveling policy, but it's consistent with what we've heard from other candidates. It's worth talking with your recruiter about the level you're targeting and how it will be assessed, so you have a shared understanding before going through the loop.

Before the technical interviews, settle the details specific to your loop.

Which rounds will I have, and how long is each one?
Will I have live coding, a take-home assignment, or both? If there's a take-home, what are the deadline and expected hours of work?
Does the role require a particular programming language?
What references and AI tools are allowed in each round?
What should I prepare for the project discussion, including slides?
What should I expect from the system-design screen and final design round?

"Having a good rapport with my recruiter was also very helpful as far as interview prep - we had a few phone calls where they helped lay out what interviewers would be looking for in the next round/s and hints on how to best prep"

— OpenAI Senior candidate

The take-home assignment

For teams that use a take-home, it comes after the recruiter conversation and before the live technical interviews. Candidates have described this sequence in both full-stack and platform SWE interviews. You build a working piece of software, then return to it with an interviewer who asks about your decisions and how you'd change the implementation.

The platform candidate's assignment was a webhook delivery service. It needed to register endpoints, accept events, retry failed deliveries with backoff, keep permanently failed deliveries in a dead-letter queue, and provide an API for checking delivery status. They had a 48-hour submission window and spent about six hours on the work, using Python, FastAPI, and SQLite. The candidate said the instructions prioritized clean code and tests over completing every feature, and they submitted through a private GitHub repository.

In the follow-up, the engineer first asked about implementation choices, including why the candidate used SQLite and what they'd change for production. They then added HMAC signature verification and event-type filtering live. The interviewer also found a worker-crash bug that left a delivery stuck in progress, which they worked through together. Expect to explain and modify the code you submit, including the parts that don't work as intended.

You work in your own development setup for the take-home, with your usual editor and local tools. That can include AI coding tools when the assignment permits them. Check the instructions for both the assignment and its live review, since permission to use AI at home doesn't necessarily carry over to the interview. You'll still need to understand, explain, and modify everything you submit.

To get a feel for this kind of assignment, try our open-ended AI coding interviews. You'll build working software in your own editor with your own AI tools, then answer debrief questions about your implementation and receive feedback. It's practice for the same build-and-review format, rather than another timed algorithm question.

Before starting the actual take-home, confirm the expected hours of work as well as the submission deadline. Also check whether your technical deep dive reviews the assignment or a project from your career. Those are different conversations to prepare for.

Coding

The live coding rounds often focus on practical implementation. Think small databases, credit-tracking services, and resumable iterators, with several behaviors to implement and test. A resumable iterator, for example, needs to save its position and later continue from that point without skipping or repeating items. Algorithmic questions still appear, including LeetCode-medium-style problems, so keep those skills in your preparation too. Plan to write the code yourself, without AI assistance.

That changes how you should prepare. Spend time building complete, testable components and extending them with new requirements. You'll still use data structures and need to explain the cost of your approach, but getting all the required behavior working can take substantial coding time even when those choices are straightforward. Being fluent in your language's standard library leaves you more time for edge cases and testing.

LeetCode is still useful here if you choose the right problems. Its Design category is closer to the implementation work you should practice for these rounds. Work through those problems without AI, then add a requirement and see how well your code accommodates it.

"I think the main challenge in my experience was the amount of code you had to output in the 1 hour session. Even when it went well and I did outline a specific / clear approach from the start I was typing the whole time and finished passing all the test cases just in time. So I would say index on getting something working quickly since there isn't some algorithmic insight you need for any of the questions, usually just a lot of functionality and test cases"

— OpenAI Senior candidate

Candidates report live coding in CoderPad, where you can run code and tests. OpenAI's stated criteria include solution design, code quality, performance, and test coverage. Some screens use a tests-first format, so practice writing runnable tests before implementing the requested behavior.

Some software engineering loops also include refactoring or debugging existing code. A full-stack candidate described simplifying a heavily nested implementation while keeping its tests passing and accommodating new requirements. Give yourself practice reading unfamiliar code and explaining the changes you make.

Can you use AI during the interview?

For L4 live coding preparation, assume no AI assistance. Practice with AI autocomplete and code-generation tools switched off so you can write, debug, and test the solution yourself.

Your round's instructions take precedence. OpenAI's company-wide policy allows different tool rules for different interviews, so use AI only if your recruiter or preparation materials explicitly permit it for that round.

Is onsite coding harder than the screen?

When the onsite includes coding, another practical implementation task is a useful expectation. The screen gives you a sense of the pace, but the topic can change and algorithmic questions can appear too. Some loops have no additional onsite coding, so use the scheduled rounds to divide your preparation time.

Most commonly asked Coding questions

OpenAI

Senior

System design

The system-design interview is a conversation about how you'd build a system and make it work under real constraints. You'll develop an architecture from a prompt, with the interviewer asking questions about your choices along the way. As the design takes shape, the discussion can move into the behavior of individual components and what happens when something goes wrong.

A good place to begin is agreeing on the scope. Ask what the system needs to do, what scale it needs to support, and what already exists. From there, you can draw a design and trace an incoming request through to the response and any stored data.

For a product design, follow a user action through the system. If a user sends a message, where is it saved? How does the response reach the browser? What happens if the connection drops halfway through? Agree on the expected behavior, then decide what belongs in the client, application server, and persistent storage.

Reported design prompts include a webhook delivery platform with retries over a 24-hour window, a Slack-like messaging service with a two-week MVP deadline, and a CI/CD system similar to GitHub Actions. These constraints give the discussion a direction. The webhook question makes delivery failures central, while the messaging question asks you to decide what a small team can realistically ship.

Once you have a workable design, be ready to follow a component down into its implementation. If you propose a queue, explain what a worker does after a crash and whether a job can run twice. If you propose caching, explain when a cached value becomes stale and whether that is acceptable. Naming a technology is only useful if you can explain the behavior you're relying on.

"I do remember the deep dives going both wide and deep - in one discussion for a notif system I said we could colocate TCP connections for all subscribers to a particular topic and we ended up talking about concurrency bottlenecks at the level of allocating file descriptors; we also talked about global replication, consistent hashing for load distribution/failover, handling bursty traffic in memory vs in a log based queue"

— OpenAI Senior candidate

You don't need to predict that exact follow-up. Practice explaining what happens inside the components you put on the whiteboard, including their limits and failure behavior.

Most commonly asked System Design questions

OpenAI

Senior

Discussing a past project

The project discussion gives the interviewer a closer look at work you've already done. You'll walk through a project you helped build, explaining the problem, your role, and the decisions that shaped the result. This is where you can give context that a coding or design exercise doesn't capture, such as why a migration had to happen gradually or how a deadline affected your approach.

Choosing the right project makes this conversation much easier. Look for substantial work where you can explain your own contribution in detail. You should be able to draw the architecture, trace an important request, and explain a decision that was difficult at the time. Be clear about the scale you actually operated at. If the interviewer asks about a larger scale, discuss what would need to change.

Candidates have described starting with a roughly 30-minute presentation followed by detailed questions. One account had 15 minutes of Q&A within a scheduled one-hour round. Build a presentation that leaves room for discussion, and follow the duration in your invitation. If you have several projects to choose from, relevance to the team can help you decide, provided you know the work well enough to answer detailed follow-ups.

"It's important to emphasize how past projects relate if you're in a team specific vs general pipeline. Since OpenAI and Anthropic basically have first pick on the whole industry right now, they can select for someone who's worked on the exact type of project / system that they're hiring for"

— OpenAI Senior candidate

Prepare a short opening that explains the problem, your responsibility, and the result. Then have details ready wherever the interviewer wants to go. You might start with why a migration was necessary and how you measured success, then spend most of the conversation explaining how you moved traffic without losing writes.

Behavioral interviews

Alongside the technical interviews, the behavioral conversation focuses on how you work with other people. You'll draw on situations from your career to explain how you made decisions, handled disagreements, and responded when a project didn't go as planned. Be ready to talk through why you acted as you did and what the people you worked with wanted to achieve.

To prepare, pick a few experiences you can discuss beyond a short summary. These don't have to come from an AI company. In a disagreement story, for example, explain what each person wanted and why, what you did to resolve it, and whether the decision worked out. Leave room for follow-up questions rather than trying to fit every detail into your opening answer.

For "Why OpenAI?", read the Charter and something recent about the team you're interviewing with. Think about what interests you and what questions you still have. If safety comes up, discuss a concrete tradeoff. For example, what evidence would you want before releasing a feature that can take actions on a user's behalf? You can reason about a problem without pretending to have worked on it.

Most commonly asked Behavioral questions

OpenAI

Senior

Where to spend your preparation time

With several kinds of interviews to prepare for, it's easy to spend most of your time on whichever feels most familiar. A practice run can help you figure out where you actually need more work.

Once you have the round list, try a timed coding session in the environment you'll use and keep track of where you stall. If you lose time on language basics, practice those directly. If your implementation passes the example but fails boundary cases, spend the next session designing tests before adding more features.

In the final days, check the practical setup. Make sure screen sharing works, you can run code and tests, and you have the right permissions for any tools you'll use. Bring your questions about the team, too. The interviews are also your chance to learn what the work would be like.