OpenAI Applied Engineer, Backend Interview Experience
OpenAI · Software Engineer
By the time I got to OpenAI, I’d interviewed with 11 or 12 companies, so my behavioral was very well rehearsed. I kept openings in my answers so if they followed up, they’d go where I wanted them to go.
Interview process
I got into the process through cold outreach plus a referral, and even that took a month or two of pushing before I got the first interview. After a pretty normal recruiter screen, I had two same-day phone rounds: one coding and one system design. The onsite was four rounds for me: coding, system design, technical deep dive on my own project, and behavioral. My big takeaway is that the coding bar is much more intense than standard big tech and feels closer to Anthropic, while the system design and behavioral pieces are more familiar. I was still waiting on the decision when we talked, and the biggest wildcard in the whole process was interviewer variability.
Interview rounds · 7
- 1
Recruiter screen
BehavioralArtificial IntelligenceI got in through cold outreach plus a cold referral, and even with multiple pushes it took about a month or two just to land the first interview. The recruiter screen itself was pretty standard, but they were clearly checking whether I had an actual point of view on AI and OpenAI, not just generic engineering interest.
Q1. What is your take on AI, and how do you think we'll use AI?
How they answeredI came in with a real view on what OpenAI is, where AI is going, and how it will influence people, not just how I'd code for it. My sense is that if you're actually passionate about AI, that just comes through in your answer. If you don't have that, it'll show pretty quickly.
- 2
Phone screen
CodingTechnicalThe first technical screen was a 60-minute coding round, and it was much more intense than normal big tech coding screens. I got the GPU credit management question, and it felt like one giant implementation problem where a tiny missed edge case could mess up everything.
Q1. Implement a GPU credit management system.
How they answeredI treated it as one very big coding problem rather than a LeetCode-style algorithm question. The hard part was planning while writing a lot of code and keeping track of all the edge cases, because one small miss can break the whole thing. My biggest takeaway was to reverse engineer off the examples they give you and make sure your code definitely works for those plus a bit more.
- 3
Phone screen
System DesignTechnicalThe other same-day phone screen was a pretty standard system design round on a job scheduler. It felt closer to a normal infra design interview than anything OpenAI-specific, but they definitely cared about scale and distributed systems depth.
Q1. Design a job scheduler.
How they answeredI approached it like a normal system design round: requirements first, then architecture, then scale and reliability. The interesting part was the distributed systems angle, like how nodes and services scale, what happens on failures, and how orchestration pieces like Kubernetes help. It wasn't tied to OpenAI's business in the prompt, but the depth they wanted was definitely infra-heavy.
Follow-up questions- What are the functional and non-functional requirements?
- How does this scale in a distributed system?
- What happens if something goes down, and how do you handle fault tolerance?
- 4
Final / onsite round
Project DiscussionPresentationTechnicalFor the technical deep dive, I picked one of my own projects and basically did a reverse system design presentation. The style was very no-BS: mostly let me talk, interrupt only if they needed clarification, and then see whether I really owned the work.
Q1. Pick a project and walk me through it.
How they answeredI presented an infra-heavy project with slides and focused on the biggest problems I ran into. Most of their questions were clarifying, like how much I owned end to end and how long it took, and then they asked what I learned. I had only planned to cover two major problems, but they wanted more, so I pulled in extra slides I'd prepared ahead of time for exactly that scenario.
Follow-up questions- Did you do this end to end?
- What was the timeline from start to finish?
- What did you learn from this project?
- What other problems came up beyond the ones you already mentioned?
- 5
Final / onsite round
BehavioralArtificial IntelligenceThe behavioral round was split pretty cleanly in half. The first half was all about OpenAI, AI, and whether my motivation was real, and the second half was standard conflict and difficult-project questions.
Q1. What is your take on OpenAI? Why OpenAI? Why AI? How can AI help, and how can it go wrong?
How they answeredI spent a big chunk of this talking about why AI matters to me, how it can help people, how it can go in a negative direction, and why I wanted OpenAI specifically. To me this felt less like a canned motivation check and more like them testing whether that interest is actually real. If you're faking it, I think they'll find that out because they want to go deep.
Follow-up questions- Why are you a fit for OpenAI and its ideals?
Q2. Tell me about a conflict or a technically complex problem you faced.
How they answeredBy this point I'd done a lot of interviews, so my behavioral stories were very well rehearsed and I already knew where follow-ups usually go. I answered with standard stories around conflict, hard technical problems, and timeline issues, and honestly this half felt easier than the technical rounds. I didn't get much pushback because I left openings in the answer for the places interviewers normally want to probe.
Follow-up questions- What happened with the timeline?
- 6
Final / onsite round
System DesignTechnicalMy onsite system design was a high-scale chat application, basically in the Slack or Teams family. Weirdly, this was the one round where the interviewer felt a bit disengaged, so I ended up driving a lot of the depth myself.
Q1. Design a high-scale chat application.
How they answeredI framed it like a WhatsApp, Slack, or Teams-type system and walked through the architecture from there. The problem itself felt practical, especially given what OpenAI already builds, but I didn't get as many follow-ups as I expected. I actually had to do a lot of the deeper exploration myself because the interviewer didn't seem to have much to add.
- 7
Final / onsite round
CodingTechnicalConceptThe final coding round was another implementation-heavy problem that was very language-specific. It started simple and then kept escalating through multiple versions, so the real test was whether I actually understood iterator internals in my language.
Q1. Implement an iterator.
How they answeredI started with a basic list iterator, then extended it to 2D, then 3D, and then an async version. It felt just like the earlier coding screen in the sense that it wasn't about memorizing algorithms, it was about writing a lot of working code and really knowing your language. I code in Python, so understanding iterator, generator, and async internals mattered a lot.
Follow-up questions- Now make it a 2D iterator.
- Now make it a 3D iterator.
- Now make it asynchronous.
Tips from the candidate
I would not prep for this like normal LeetCode and call it a day. For coding, I would go deep on language internals, especially if you're using Python: iterators, generators, async, concurrency, that sort of stuff. For system design, I would make sure I actually understand distributed systems internals, scale, fault tolerance, and technologies like Kubernetes, not just high-level boxes and arrows. For the project deep dive, I would prepare extra material beyond the obvious happy path because they may ask you to keep going. And for the motivation side, if you're genuinely into AI it'll show, but if you're trying to fake it, there's a decent chance they'll catch that.
Company culture
My read is that they care a lot about whether you can independently solve hard problems, not whether someone can mentor you into the answer. The process felt less standardized than a place like Meta, and because the interviewer pool is smaller relative to demand, I think you see more variability, including the occasional disengaged interviewer. They seem to rotate through a question bank pretty fast, so leaked questions help, but only for a while, and the coding rounds still demand real depth. Also, they decide level after the loop, and there was zero comp discussion during my process, which to me felt like they're confident enough in their pay that they don't need to sell it early.