OpenAI Software Engineer Interview Guide

OpenAI's software engineer (SWE) interview is built around practical engineering work, with coding rounds that ask you to recreate systems that already exist and system design rounds that push hard on scale at every turn.

The deepest signal comes from the technical deep dive, where interviewers move past polished summaries to question what you built, why, and how you worked with the people around you.

This guide breaks down each stage of the OpenAI Software Engineer interview process, what interviewers look for, and how to prepare with real example questions, actionable tips, and resources.

OpenAI Software Engineer interview process

The OpenAI SWE interview process moves quickly and varies more than the loops at other established big-tech companies, with composition shifting between teams, and even between candidates for the same team. Recent loops have wrapped within roughly a month once interviews begin.

Here's an example of what the interview process can look like:

• Recruiter screen: A short conversation covering background, motivation, and AI-related interests
• Technical phone screen: Two 60-minute rounds scheduled on the same day, one coding and one system design, each with a different interviewer
• Virtual onsite: 4-5 rounds including one coding interview, one system design interview, a technical deep dive on a project you've worked on, and one or two behavioral rounds

Recruiter screen

The OpenAI recruiter screen is a short, relatively informal conversation that filters for motivation and AI fluency. Expect questions about your background, why you want to work at OpenAI, and where you think AI is headed, with the recruiter listening for whether you have a real point of view on the technology.

When OpenAI reaches out to you first, the screen is often run by a third-party contractor before an OpenAI recruiter takes over for the rest of the loop. Referral and inbound paths typically connect you with an OpenAI recruiter from the start.

Compensation rarely comes up at this stage; if it does, stay non-committal and push the conversation to later in the process.

Interviewers look for:

• Genuine interest in AI: Whether you can speak to where the technology is going and why it matters
• Clear motivation for OpenAI specifically: Why this company over the rest of the AI lab landscape
• Background fit for the role: How your past work maps to the kind of engineering OpenAI does
• Ability to talk about a recent project: A concise account of something you built and why it mattered

Recently asked questions

Here are real, recent interview questions reported by candidates:

• Walk me through a recent project you worked on and why it mattered.
• Why are you interested in OpenAI?
• What's your take on where AI is headed?
• Where do you see AI going wrong, or being misused?

Technical phone screen

OpenAI's SWE technical phone screen runs as two 60-minute rounds on the same day, one coding and one system design, each with a different interviewer.

Expect practical coding prompts, which often involve interactions with stubbed services or rebuilding behavior from a real system. System design prompts skew toward well-known products at scale, with interviewers pressing on how your design holds up at 100x or 1000x the original load.

Interviewers look for:

• Practical problem-solving: How you approach prompts that don't map cleanly to any standard pattern
• Edge case discipline: Whether you identify failure modes and test cases before writing code
• Scalability instincts in system design: How your design absorbs aggressive growth in users, traffic, or data volume
• Iterative optimization: Whether you can land a working solution first, then layer in performance improvements when pressed
• Clear reasoning under unfamiliar constraints: How you talk through a problem when the prompt is something you haven't seen before

Sample questions

Here are some real interview questions reported by candidates:

• Design a job scheduler, with follow-ups on distributed execution, fault tolerance, and orchestration internals.
• Build a system to manage GPU credits across multiple companies with widely varying usage patterns.
• Implement an efficient way to sync key/value state to and from the cloud, using provided helper methods that convert strings to bytes and back.

Coding interview

The OpenAI SWE onsite coding round is a 60-minute interview built around recreating something that already exists. Expect prompts modeled on real systems, with the interviewer pressing on edge cases and optimizations as you go.

The pattern across recent loops is consistent: identify the test cases up front, get a working solution in place, then iterate. Plan to write a significant volume of code; interviewers escalate through stages, often pushing into caching, invalidation, or async behavior once the base solution holds.

Interviewers look for:

• System replication instincts: How you approach prompts that mirror real systems
• Test case discipline up front: Whether you map out edge cases before writing code
• Layered approach to optimization: How you sequence your approach when the prompt has multiple layers
• Depth in language internals: Your fluency with the underlying mechanics of your chosen language
• Targeted optimization under follow-up: How you respond when asked to layer in caching, invalidation, or scale considerations

Recently asked questions

Here are real, recent interview questions reported by candidates:

• Implement spreadsheet-style cell notation where formulas can reference other cells, with updates propagating to dependent cells when a referenced cell changes.
• Build an iterator that outputs values in sequence, then extend it to a 2D iterator, then to an async iterator.
• Walk through where you'd add caching to your solution, what to cache, and when to invalidate.

System design interview

OpenAI's SWE onsite system design round runs 60 minutes and centers on how your design absorbs aggressive growth in users, traffic, and data.

Recent prompts have covered chat applications, streaming platforms, and infrastructure-style problems like job schedulers. Once a base design is in place, expect the interviewer to push into specific growth scenarios, with follow-ups on fault tolerance, distributed coordination, and the inner workings of orchestration systems like Kubernetes.

Interviewers look for:

• Scalability instincts under aggressive growth: How your design adapts when users, traffic, or data volume jumps by orders of magnitude
• Depth on distributed system mechanics: Your fluency with concepts like fault tolerance, replication, and orchestration internals
• Iterative deepening: Whether you can land a viable design first and then layer in complexity as the interviewer pushes
• Real-world infrastructure reasoning: How you think about real failure modes
• Clear prioritization of trade-offs: How you handle competing concerns like consistency, latency, and cost as scale increases

Recently asked questions

Here are real, recent interview questions reported by candidates:

• Design Slack, with follow-ups on how the system scales by 100x, 1000x, varying frame rates, and users across the world.
• Design a streaming platform that holds up under aggressive growth in concurrent users and data throughput.

Technical deep dive (project presentation)

The OpenAI SWE technical deep dive is a 45-minute round where you present a project you've worked on and defend the decisions behind it. You bring the architecture, and the interviewer pushes on how it got built and why you made the calls you made.

Rapid follow-up is the defining feature of this round. Overly structured answers and headline metrics get treated as setup; OpenAI interviewers cut in fast to ask what you did, why, who you worked with, and what made the technical and stakeholder challenges hard.

Interviewers look for:

• Direct ownership of the work: What you personally built, decided, or led, separate from what the broader team contributed
• Reasoning behind technical decisions: Why you chose the approach you did and what alternatives you considered
• Depth on stakeholder and cross-functional challenges: How you navigated competing priorities, working relationships, and non-technical complexity
• Lessons learned and what you'd change: Whether your reflection identifies real trade-offs and decisions you'd revisit, beyond surface-level takeaways
• Comfort with rapid follow-up: How you respond when the interviewer pushes past your prepared narrative into unscripted territory

Recently asked questions

The round opens with a request to walk through a recent project you're proud of, and the rest of the time is spent on follow-up. Recent candidates were asked:

• Did you work on this project end to end?
• How long did the project run from start to finish?
• What were the technical challenges, and how did you handle them?
• Who did you collaborate with, and what was that working relationship like?
• What did you learn from this project, and what would you do differently today?

Behavioral interview

OpenAI's SWE behavioral interview splits roughly in half. The first half centers on motivation and cultural fit, with extended discussion of why OpenAI, why AI, and how you think about the technology's trajectory. The second half covers common behavioral questions about conflict, collaboration, and how you've handled difficult situations on past projects.

The cultural-fit half goes deeper than a typical behavioral round. Interviewers listen for a real point of view on AI and watch for whether you operate as a generalist who can write docs, solve problems, and code with comfort across all three modes.

Interviewers look for:

• A clear point of view on AI: Whether you can speak to where the technology is going, how it should be used, and how it could go wrong
• Generalist instincts: Comfort moving between writing, problem-solving, and coding with depth in each
• Concrete handling of conflict: How you've worked through disagreement with peers, managers, or stakeholders on real projects
• Cross-functional fluency: How you've collaborated with non-engineering teams when priorities don't align

Sample questions

Here are some real interview questions reported by candidates:

• What specifically draws you to OpenAI's mission, and how does it connect to your past work?
• How do you think AI could go wrong, and what role do engineers play in preventing that?
• Tell me about a time you faced a conflict with a coworker or stakeholder.
• Tell me about a time you worked with a legal team or another function outside engineering.
• What does a standard day as a software engineer look like for you?

How to prepare for the OpenAI Software Engineer interview

1. Practice practical coding work: Recreate behavior from real systems, plan test cases before writing code, and layer optimizations like caching and invalidation on top of a working baseline.
2. Study the internals of your primary language: For Python, work through iterators, generators, and async constructs in depth, since onsite coding rounds may ask you to implement these directly.
3. Prepare project deep-dives that hold up under rapid follow-up: Pick projects you can speak to in depth, with answers ready for what you personally did, why you made the calls you made, who you collaborated with, and what you'd do differently today.
4. Prepare more material than you expect to use: Interviewers often push past prepared answers, especially in the technical deep dive, so have additional challenges, decisions, or trade-offs ready to surface on demand.
5. Demonstrate complexity through markers other than scale: Heavy dependency surfaces, tight delivery windows, ambiguous starting conditions, new technology under pressure, and competing priorities across peer teams all carry weight here.
6. Structure behavioral prep around the two halves of the round: The first half tests motivation and AI fluency, so develop a real point of view on where the technology is headed and where it could go wrong. The second half covers standard behavioral questions, so prepare concrete conflict and cross-functional examples that show generalist instincts and real ownership.
7. Practice with an OpenAI-specific coach: Run through coding, system design, and project deep-dive rounds with someone who can press on your reasoning the way OpenAI interviewers do. Rapid follow-up is the hardest part of this loop to prepare for alone.

About the OpenAI Software Engineer role

Many OpenAI Software Engineers sit within Applied AI or Applied Engineering, the broad organization covering engineering work outside core model research.

Leveling is decided after the interview loop, with senior and staff candidates running through the same process and the level assigned based on performance. Recent candidates have come from backend and distributed systems backgrounds, with day-to-day work spanning infrastructure, API services, and the engineering layer that supports OpenAI's products.

OpenAI Software Engineers typically work on:

• Backend systems supporting OpenAI's products, including infrastructure work that spans the engineering layer between core models and the product surface
• Engineering work that touches scale and distributed systems, since the products SWEs support operate at OpenAI's level of growth
• Cross-functional collaboration with adjacent teams, including legal, where engineers carry more scope than at most established companies
• Generalist engineering work that spans writing documentation, problem-solving, and coding

OpenAI Software Engineer experience requirements

OpenAI Software Engineers are typically experienced backend or distributed systems engineers. The interview process tests for depth in language internals, system design at scale, and the ability to work autonomously on novel challenges.

Additional resources

• OpenAI Software Engineer interview questions
• OpenAI Software Engineer Applied AI interview experience (November 2025)
• System Design Interview course
• SWE Behavioral Interview course
• SWE Coding Questions course
• OpenAI Full Stack Engineer Interview Guide
• OpenAI Research Engineer Interview Guide
• OpenAI company page

FAQs about the OpenAI Software Engineer interview

How long is the OpenAI Software Engineer interview process?

The OpenAI SWE interview process typically runs about a month from the recruiter screen to the final round, though timelines vary based on scheduling and candidate availability.

Does OpenAI use leveling for Software Engineers?

OpenAI uses internal leveling, but the level isn't assigned until after the SWE interview loop. Senior and staff candidates run through the same interview process, and the level is determined based on performance across the loop. Recent candidates have been leveled at L2 through L6.

Does OpenAI's recruiter contact you through a third party?

OpenAI's recruiter contacts candidates through a third party in some cases, depending on how you entered the pipeline. When OpenAI sources candidates through outbound recruiting, initial outreach often comes from a third-party recruiting contractor before being handed off to an internal OpenAI recruiter. Referral and inbound paths typically connect you with an OpenAI recruiter from the start.

How much does an OpenAI Software Engineer make?

Here are the reported compensation ranges by level for OpenAI Software Engineers, according to levels.fyi:

• L2 (Entry level): ~$249K
• L3: ~$337K
• L4: ~$569K
• L5: ~$1.15M
• L6: ~$1.29M