OpenAI Software Engineer Interview Guide

The short answer: OpenAI publishes one interview guide for all roles. It lists a resume review, an introductory call with a recruiter or hiring manager, a skills-based assessment that varies by team (pair coding, a take-home project or a technical test), and final interviews of 4 to 6 hours with 4 to 6 people over 1 to 2 days. OpenAI says some interview formats allow AI tools and others do not, and each interview's prep materials say which. Candidate reports describe practical, multi-part coding problems, a system design round and a deep dive on a past project in the finals. Last reviewed October 2026.

OpenAI software engineer interview: quick answers

The OpenAI software engineer role

What OpenAI engineers work on, the skills its job postings ask for, and what a normal week looks like.

OpenAI software engineer levels and pay

Your level sets the bar you are interviewed against and the pay band of the offer.

OpenAI does not publish its engineering levels, and its postings carry no level numbers. Level names such as L2 to L7 come only from third-party data, so the table below uses posting titles. Candidate reports say the level is decided after the loop and that down-leveling is common.

Pay in OpenAI's US job postings

OpenAI's US postings show a compensation range for base salary, and almost every software engineer posting adds "Offers Equity". The two Principal postings at $459K to $536K show no equity line.

Rows use posting titles because OpenAI postings state no levels. Ranges are wide, and one range is shared by postings that ask for 3+ to 9+ years, so a single band likely covers several internal levels. Ranges seen October 27, 2026.

OpenAI software engineer interview process

The steps below follow OpenAI's interview guide, which covers every role. Detail on what happens inside each round comes from candidate reports.

Based on OpenAI's own pages and candidate reports.

OpenAI says each stage usually turns around within about a week, and references may be requested before an offer. Candidate guides disagree on team matching: one says you apply to a specific team and meet your prospective manager, another says matching can happen after the offer. Last reviewed October 2026.

What is different at OpenAI

• AI tool rules change by round. OpenAI says some interview formats intentionally allow AI tools while others assess problem-solving without them, and each interview's prep materials spell out the rule. Candidate reports describe a beta final round where you work with an AI coding agent on a large codebase.
• Coding looks like real work. Candidate reports describe multi-part problems such as a versioned store or spreadsheet formulas, with tight timing. OpenAI says it grades design, code quality, performance and test coverage, so tests are part of the answer.
• The skills assessment is chosen by the team. OpenAI says it can be pair coding, a take-home project or a technical test, and some roles have more than one.
• A past project gets its own round. Candidate reports describe a presentation or deep dive on a technical project in the finals, with probing on what you personally did.
• Mission comes up in behavioral rounds. OpenAI recommends reading its Charter, and candidate reports say interviewers ask for your views on where AI is heading, its risks and safety.
• OpenAI says it is not credential-driven. It values high potential, which it describes as a proven ability to ramp up quickly in a new domain and produce results.

OpenAI software engineer interview questions

Coding problems from our bank tagged to OpenAI, then design, fundamentals and behavioral prompts we wrote for this guide.

1from typing import List, Tuple
2
3def admit(requests: List[Tuple[int, str, int]], limit: int, window: int = 60) -> List[bool]:
4    """Return whether each request is admitted under the per-org token budget."""
5    pass

1from collections import defaultdict, deque
2from typing import List, Tuple
3
4def admit(requests: List[Tuple[int, str, int]], limit: int, window: int = 60) -> List[bool]:
5    recent = defaultdict(deque)  # org -> deque of (timestamp, tokens) for admitted requests
6    used = defaultdict(int)      # org -> tokens admitted inside the current window
7    out = []
8    for t, org, tokens in requests:
9        q = recent[org]
10        # Drop admitted requests that are now outside (t - window, t].
11        while q and q[0][0] <= t - window:
12            used[org] -= q.popleft()[1]
13        if used[org] + tokens <= limit:
14            q.append((t, tokens))
15            used[org] += tokens
16            out.append(True)
17        else:
18            out.append(False)
19    return out
20
21# Complexity: O(n) time overall, since each request enters and leaves a deque once; O(n) space.

How to prepare

A plan for the OpenAI loop, most important first:

1. Practice practical, multi-part coding problems against a timer, and write tests as part of every solution.
2. Prepare a 45-minute deep dive on one past project: the problem, your design, the trade-offs and exactly what you built.
3. Practice system design for API, streaming and infrastructure problems, and be ready to defend every tool you name.
4. Read the prep materials for each round, including the AI tool rule, and ask your recruiter if anything is unclear.
5. Read the OpenAI Charter and recent blog posts for your team, and form your own view on AI's progress and risks.
6. Brush up on Python internals such as iterators, generators and async, which candidate reports say come up.

Practice path

Courses that cover the rounds above:

• Data Structures and Algorithms
• System Design
• Python Basics
• GPU Programming