OpenAI Full Stack Engineer Interview Guide
Updated by OpenAI candidates
Our guides are created from recent, real, first-hand insights shared by interviewers and candidates. If your experience differs, tell us here.
OpenAI’s Full Stack Engineer interview process is more demanding than interviews for the same role with other big tech companies. Instead of testing execution alone, OpenAI's interview tests whether you can think above code, design for consumer scale, reason about AI-generated code, and refactor for abstraction.
This guide breaks down each stage of the OpenAI Full Stack Engineer interview process, what interviewers look for, and how to prepare with real example questions.
OpenAI Full Stack Engineer interview process
The OpenAI Full Stack Engineer interview process consists of two stages: phone screens and an onsite loop. The full process typically takes two weeks to several months, depending on team availability.
Here’s an example of how the interview process breaks down:
- Recruiter screen: A short discussion about your background, interest in OpenAI, and fit for the role
- System design screen: A 60-minute end-to-end design of an existing OpenAI product, including UI and frontend considerations
- Coding screen: A 60-minute algorithmic problem tied to a real OpenAI product or use case, with test cases of increasing complexity
- Coding round (onsite): A refactoring exercise in which you extend existing code to meet new requirements
- Past project presentation (onsite): A 60-minute technical deep dive into a project you've chosen, with a focus on scale and design decisions
- Leadership conversation (onsite): A 60-minute behavioral interview focused on conflict resolution and collaboration
OpenAI has a team-dependent interview process, with wide variation across teams. Because there isn’t a typical OpenAI Full Stack interview process, this guide provides the closest approximation.
Recruiter screen
The OpenAI recruiter screen is a short introductory call covering your background, your interest in OpenAI, and a high-level overview of the interview process. Interviewers are primarily gauging your AI familiarity and your specific motivation for OpenAI.
Recently asked questions
Here are some real interview questions reported by candidates:
Asked at System design screen
The OpenAI system design screen is a 60-minute interview where you design an existing OpenAI product end-to-end, including frontend wireframes and UI. It's broader than a typical system design round: interviewers expect product thinking alongside technical architecture.
The screen is one of two back-to-back phone interviews, separated by a 15-minute break. Expect the prompt to be tied directly to an OpenAI product or a generic system.
Interviewers look for:
- End-to-end design coverage: A complete design from UI and wireframes through to the API and data layer.
- Frontend thinking: Most backend-focused engineers underweight the UI. One candidate reported the interviewer explicitly redirected toward the user experience before diving into infrastructure.
- Product judgment: The prompt involves a developer-facing tool. Interviewers want to see that you understand who the user is and what they need.
- Appropriate abstraction: You don't need to design the model layer. Treat it as a black-box API. But the interviewer won't tell you this unprompted.
Ask the interviewer explicitly whether you need to design the model infrastructure or can abstract it away. The interviewer won't flag this as out of scope on their own, and building it out wastes time you need for the frontend and API layers.
Recently asked questions
Here are some real interview questions reported by candidates:
Asked at Coding screen
The OpenAI coding screen is a 60-minute algorithmic interview where you might build a solution to a problem tied directly to an OpenAI product or use case. You'll be given test cases of increasing complexity to run your solution against.
If the problem domain is OpenAI-specific, expect something in the range of credit issuance, token management, or API usage tracking, where the rules governing the data add meaningful complexity to the algorithm.
Interviewers look for:
- Problem scoping: Ask clarifying questions early. If the problem is OpenAI-specific, the scope of each test case may not be obvious without prompting.
- Reasoning transparency: Talk through your approach as you build. Interviewers want to follow your thinking, not just see the output.
- Test case coverage: There's a base minimum set of test cases interviewers expect you to hit, followed by progressively harder ones. Practice against the OpenAI SWE question bank to get a feel for the complexity range.
- AI tool usage: If they allow AI coding assistants and search engines, still narrate what you're doing. OpenAI's coding rounds prioritize reasoning transparency over AI-native workflows; don't use AI to generate a complete solution, use it as a reference and show your thinking throughout.
Recently asked questions
Here are some real interview questions reported by candidates:
Asked at OpenAI • Onsite loop
The OpenAI onsite loop can be completed virtually or in person. The number of rounds can vary, with a sample loop like this:
- Coding round: A refactoring exercise where you extend existing code to meet new requirements
- Past project presentation: A 60-minute technical deep dive into a project you've chosen, with a focus on scale and design decisions
- Leadership conversation: A 60-minute behavioral interview focused on conflict resolution and collaboration
Coding round (onsite)
The OpenAI onsite coding round is a refactoring exercise, not a fresh algorithmic problem. You'll be given roughly 100-120 lines of intentionally convoluted code, with deeply nested conditionals, and asked to extend it to meet new requirements.
The existing code passes a set of tests. Your job is to maintain that test parity while refactoring the structure and passing a new set of tests. Interviewers are evaluating whether you can think one abstraction layer above the immediate problem, not just patch it to pass the next case.
Interviewers look for:
- Abstraction thinking: Can you identify the right class structures and design patterns to make the code extensible, not just functional?
- Test parity: The refactored code must continue to pass all existing tests while also handling new requirements.
- Reasoning transparency: Talk through your approach as you restructure. Interviewers want to follow your logic, not just see the output.
- Comfort with messy code: The starting code is purposely difficult to read. Expect deeply nested conditionals and convoluted logic. A recent candidate described it as "eight levels of if-nesting."
Recently asked questions
A recent candidate reported being asked to refactor a credits management system: given a set of rules governing credit issuance, expiration, and usage, clean up a convoluted implementation and extend it to handle new requirements. The deep dive focused on identifying the right abstraction layers and class structures to make the system extensible without breaking existing test parity.
Past project presentation
The past project presentation is a 60-minute interview where you present a project of your choosing to a single technical interviewer. Scale is the primary filter; one candidate's post-interview feedback confirmed that a technically strong project built for a small customer base wasn't enough if it couldn't speak to massive user demand.
Prepare slides. The interviewer will direct the conversation toward specific design decisions, asking why you chose one approach over another and how your choices hold up at scale. If your project wasn't built for scale, prepare a credible answer for how it would get there.
Interviewers look for:
- Scale evidence: Has this system handled, or been designed to handle, massive user demand? If not, can you articulate a credible path to get there?
- Design decision rationale: Why did you choose this architecture, storage approach, or framework over the alternatives?
- LLM usage: If your project involved AI, be ready to explain which model you used, why you chose it over others, and how you structured your evals.
- Depth under pressure: Interviewers will dig into the details of your decisions. Prepare to defend your choices, not just describe them.
Recently asked questions
Here are some real interview questions reported by candidates:
Asked at 
Asked at Leadership conversation
The leadership conversation is a 60-minute behavioral interview with a manager or executive, focused primarily on conflict resolution and cross-team collaboration.
One candidate was told directly that navigating conflict is a top priority at OpenAI because of the talent density and pace of growth. Interpersonal friction is inevitable, and OpenAI wants to know you can handle it.
The conversation will also cover your perspective on AI, including where you see it heading and what challenges OpenAI might face. Despite being a common prep area, AI ethics questions may not come up as a formal topic. A candidate reported only a brief conversational exchange about AI's capabilities and limitations, not the structured moral dilemma format some candidates prepare for.
Interviewers look for:
- Conflict resolution: A specific, detailed account of a conflict you navigated, including your decision-making and how the resolution took shape
- Cross-team collaboration: Experience working across teams or organizations with competing priorities
- AI perspective: A considered, informed view of where AI is heading and what limitations or challenges the field faces
Recently asked questions
Here are some real interview questions reported by candidates:
Asked at OpenAI Full Stack Engineer interview prep
The OpenAI Full Stack Engineer interview rewards full-stack range, abstraction-layer thinking, and the ability to communicate design decisions under pressure. Interviewers are evaluating whether you've built systems that handle massive demand, or can credibly speak to how you'd get there.
Common mistakes to avoid in the OpenAI Full Stack Engineer interview
- Neglecting the frontend: Most backend engineers underweight the UI in the system design screen. The prompt is tied to a real OpenAI product, and interviewers will explicitly redirect toward the user experience. Treat the wireframes and frontend layer as a first-class deliverable, not an afterthought.
- Presenting a project that can't speak to scale: A technically sound project built for a small customer base can fail this round if you can't articulate a credible path to massive user demand. Prepare a direct answer for how your project would scale, even if it wasn't built to.
- Patching instead of abstracting: The onsite coding round is a refactoring exercise, not a fresh algorithm problem. Interviewers are looking for one abstraction layer above the immediate fix. Candidates who clean up the code without rethinking the underlying structure miss what the round is testing.
How to prepare for the OpenAI Full Stack Engineer interview
- Practice reading and refactoring messy code: The onsite coding round gives you roughly 100-120 lines of intentionally convoluted code with deeply nested conditionals. Practice untangling dense logic quickly, identifying the right class structures, and extending a system without breaking existing test parity.
- Prepare your scale story: For the past project presentation, know your design decisions cold and have a prepared answer for how your system handles, or would handle, massive user demand. If your project was small-scale, prepare a specific, credible answer for how it would get there. Post-interview feedback from a recent candidate confirmed this was a deciding factor.
- Scope before you build: Both the system design screen and the coding rounds reward candidates who ask clarifying questions early. In the system design screen, asking whether to abstract the model infrastructure is the difference between spending time on the right layers and going down a trap. The interviewer won't redirect you unprompted.
- Prepare conflict examples with depth: The leadership conversation focuses heavily on conflict resolution. Practice defending your approach to past decisions with specificity. A recent candidate was told directly that navigating interpersonal friction is a top priority at OpenAI given the talent density and pace of growth. Use mock interviews to pressure-test your conflict stories before the real thing.
About the OpenAI Full Stack Engineer role
OpenAI Full Stack Engineers work across the full organization: B2B and B2C product development, security, frontend, infrastructure, and AI tools.
What OpenAI Full Stack Engineers do and who thrives in the role
- Autonomous end-to-end ownership: Full Stack Engineers are given significant autonomy and are expected to take charge of ambitious projects without relying on process or extensive oversight.
- Speed and focus: OpenAI favors engineers who move fast and pursue tasks aggressively. Excessive deliberation is a cultural mismatch.
- Cross-functional collaboration: Full Stack Engineers work alongside researchers, data scientists, and GTM teams. Clear communication about technical concepts and limitations is expected.
OpenAI Full Stack Engineer experience and education requirements
OpenAI hires Full Stack Engineers with at least two years of experience and favors candidates with startup or early-stage company backgrounds. Team-specific roles require direct experience with the relevant discipline or language. AI model-building experience may not be required, but you should be comfortable working with AI-generated code and OpenAI's tools.
OpenAI doesn’t list any formal education requirements for Full Stack Engineer roles.
OpenAI Full Stack Engineer interview resources
- OpenAI Careers Page
- OpenAI Interview Guide
- Software Engineering Interview Prep
- Software Engineer Interview Questions
- OpenAI Interview Questions
- Generative AI Interview Prep
FAQs about the OpenAI Full Stack Engineer interview
How long is the OpenAI Full Stack Engineer interview process?
The OpenAI Full Stack Engineer interview process typically takes 2-8 weeks from recruiter screen to final decision, though timelines vary by team availability.
Does OpenAI have internships?
OpenAI offers internships, early career opportunities, and research residencies through its Emerging Talent program. You can apply directly through their careers page.
Do I need AI experience to work at OpenAI?
You may not need experience building or training AI models to work as an OpenAI Full Stack Engineer. You'll be expected to understand OpenAI's products and be comfortable working with AI-generated code.
How much do OpenAI Full Stack Engineers make?
OpenAI Full Stack Engineer compensation varies significantly by level. According to Levels.fyi, reported total compensation ranges from around $249,000 at L2 to $1,390,000 at L5.
- L2: $249,000
- L3: $321,000
- L4: $678,000
- L5: $1,390,000
Learn everything you need to ace your Full Stack Engineer interviews.
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