Introduction#
There is a specific kind of candidate who struggles at OpenAI: the engineer who has drilled four hundred LeetCode problems, can recite the design of a URL shortener from memory, and has already passed a Meta or Google loop.
That profile is optimized for a format OpenAI largely does not run. The rounds look superficially similar — coding, design, behavioral — but they are scored on different axes. This post is about those axes, and about the four ways strong engineers reliably lose the loop.
OpenAI publishes no rubric. What follows is inferred from public candidate reports and prep guides from 2025 to late 2026, plus patterns we see in mock interviews with candidates coming out of these loops. It is a well-informed reading, not an official one.
What They Actually Evaluate#
Working Code, Not Correct Answers#
The most consistent signal across reports: OpenAI coding rounds favor building something that runs over arriving at an optimal solution.
The evaluation dimensions candidates report being scored on are solution design, code quality, performance, and test coverage. That last one is unusual and it tells you a lot. Most big-tech coding rounds never look at your tests. If tests are a scored dimension, then an engineer who finishes with a working function and three meaningful tests outscores one who produces an elegant unfinished optimum.
What this punishes: people who spend eighteen minutes in silence searching for the clever approach. What it rewards: people who get a brute-force version running in ten minutes, name its weakness out loud, and then improve it.
Whether Your Code Survives a Moving Requirement#
Practical rounds are built around extension. You solve the thing, and then the interviewer changes the thing.
They are not checking whether you can handle the new requirement. They are checking what your first structure cost you. An engineer who hardcoded assumptions into a single 60-line function now has to rewrite; an engineer who separated parsing from logic adds a parameter. Both wrote working code. Only one of them demonstrated judgment.
This is the clearest difference from puzzle-style interviewing, where your solution is thrown away the moment it is correct.
Scoping Under a Real Constraint#
Several reported design prompts attach a deadline — the most-cited example being a messaging service with a two-week MVP window.
That reframes the entire round. Scale is no longer the goal; choosing what not to build is the goal. The candidate who draws Kafka, a service mesh, and multi-region replication has failed the actual question, however good the architecture is. The candidate who ships a single service with a Postgres table, says plainly "here is the first thing that breaks at 10x and here is what I would do about it then," and spends the remaining time on the delivery plan, has answered it.
Reasoning About AI Systems as Systems#
If the team is AI-adjacent, design prompts drift toward inference serving, retrieval, evals, and agent infrastructure. The bar here is not research knowledge. It is whether you treat a model as a component with real properties: latency distribution, batching behavior, GPU memory ceiling, cost per token, non-determinism, and no clean correctness oracle.
The tell that separates candidates is evaluation. Asked to design anything LLM-shaped, most people design the serving path and stop. The strong answer includes how you would know whether a change made the system better — offline eval set, online metric, human review sample, and what you would do when those three disagree.
Depth of Genuine Ownership#
The deep dive is an interrogation, and it keeps going until it finds your limit. The scoring is effectively "how far down can this person go on something they claim to have built."
Picking your most impressive project is usually the wrong move. Pick the one where you made the decisions, hit the failures, and still remember the details. Impressiveness has a ceiling; depth does not.
What They Don't Test (as Much as You Think)#
- Pattern recall. Memorized DP recurrences and the thirty-problem sliding-window canon carry far less weight here than at companies with standardized coding rounds.
- Textbook scale numbers. Nobody is impressed by "we need sharding" if the scope never justified it.
- Rehearsed behavioral narrative. The behavioral round is real but comparatively light. A polished STAR performance with no substance underneath is less useful than specific, slightly messy honesty.
- Prestige of your employer. Depth of ownership beats logo quality in the deep dive, consistently.
The Four Rejection Patterns#
1. Silent Optimization#
The most common. The candidate recognizes that a better solution exists, goes quiet to find it, and surfaces at minute 25 with nothing on screen. In a round scored on working code and tests, twenty-five silent minutes is unrecoverable even if the eventual answer is optimal.
The fix: get something running inside the first third of the round, out loud. "Let me get a correct version down first, then tighten it" is a sentence that buys you the whole interview.
2. Architecture Theater#
Answering a scoped design prompt with a maximal architecture. It reads as pattern-matching: this is a design interview, design interviews want distributed systems, here is a distributed system.
The fix: before drawing anything, say out loud what the constraint is and what you are deliberately leaving out. "Given two weeks, I'm not building a queue — I'll do synchronous writes and show you where that breaks" is a stronger opening than any diagram.
3. Designing the Happy Path of an AI System#
Building the inference path, the prompt flow, the retrieval step — and never addressing how you would detect regression, handle a model that is confidently wrong, or bound cost. At a company whose entire business is operating models in production, this gap is conspicuous.
The fix: make evaluation and failure handling a standing section of every AI-system answer, the way you would include monitoring in an infrastructure answer.
4. Generic Motivation#
"I'm excited about AGI" is the baseline answer from essentially every applicant, which makes it a non-answer. It does not sink a strong loop, but it costs you on a borderline one.
The fix: one specific, true thing. A paper you read and disagreed with, a product behavior you noticed and have an opinion about, a problem in your current job that this team's work would have solved. Specificity is the entire signal.
A Self-Check Before Your Loop#
FAQ#
Is the OpenAI interview harder than Google's or Meta's?#
Different, not uniformly harder. The algorithmic ceiling is usually lower; the bar on shipping working code, scoping judgment, and project depth is higher. Candidates who have optimized hard for standardized big-tech loops tend to find it harder, because less of their preparation transfers.
How much do I need to know about machine learning?#
For most software engineering roles, you do not need to train models. You do need to reason about a model as a production component — latency, cost, batching, non-determinism, and evaluation. If the role is explicitly ML or research engineering, that changes.
What is the hardest round?#
Candidate accounts most often name the deep dive, because it is the one you cannot prepare for by practicing a format. It scales with how much you genuinely owned.
Does strong performance in coding compensate for a weak design round?#
Assume not. Reports across AI labs describe loops where a clear failure in one technical round ends the process, regardless of the rest. Treat every round as independently gating.
How We Compiled This#
OpenAI does not publish evaluation criteria, so this post synthesizes publicly available third-party accounts — candidate write-ups and interview-prep guides published between mid-2025 and late 2026 — together with patterns from mock interviews we run with candidates in these loops. Sources disagree on specifics, and formats shift by team, level, and quarter. Confirm your own loop's shape with your recruiter.