2024 vs 2025 vs 2026
Interview Experience
2024 (applied from Jan 2024 - May 2024) - Basically my first time applying to stuff. Before this I didn't even realize you had to apply online and that handing out my resume at my university ca
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2024 (applied from Jan 2024 - May 2024) - Basically my first time applying to stuff. Before this I didn't even realize you had to apply online and that handing out my resume at my university career fair didn't do anything most of the time lol. Applied slowly in Jan/Feb and really ramped up in Mar/Apr after sorta getting the gist of it * # apps - 366 (intern) * offers - $20/h 2025 (applied from Sep 2024 - May 2025) - Had a pretty good idea of how recruiting worked now and how much I needed to apply.
Return offer happened in the middle, so afterward I only aimed for stuff that paid higher * # apps - 97 (intern + new grad) * offers - $50/h parttime, $92k return (also: graduated undergrad at my state school, started grad in a specialization at an ivy) 2026 (applied from Aug 2025 - Nov 2025) - Only aimed for stuff that paid higher than $50/h base (and otherwise I'd just continue the salaried job anyway) so that naturally resulted in me aiming kinda high. Honestly I was sorta not taking anything seriously at this point in time so I really didn't apply much at all compared to previous seasons, and me being pleasantly surprised by my callback rate pretty early on just made me even more selective lol * # apps - 35 (intern + new grad) * offers - $60/h OpenAI intern, \~$240k faang+ and startup Pretty happy with what I got so I might be done with recruiting for a while, assuming I don't get laid off instantly. ofc I'm not a recruiter so I can't say with any authority what got me past resume screens/interviews and stuff, but I can try to answer questions about my CS/programming/interviewing experience (or what I can remember lmao)
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About OpenAI Interview Reports
This question was reported by a candidate who interviewed at OpenAI. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at OpenAI are the higher-signal extractions to take from this report.
For broader preparation context, the OpenAI interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in OpenAI reports consistently are the ones worth investing in; one-off niche problems are not.
During Your OpenAI Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in OpenAI reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.