OpenAI Interview Questions — Page 19

OpenAI·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Behavioral round for an iOS Engineer role at OpenAI. Pretty standard stuff covering the usual leadership and conflict topics, nothing that should blindside you if you've done this kind of prep before.

Tell me about a time you took ownership of something that wasn't strictly your responsibility.
Describe a situation where you had a conflict with a teammate or stakeholder. How did you handle it?
How do you operate when requirements or goals are unclear? Give a specific example.
Tell me about a time you led a project or initiative, even without a formal leadership title.
How do you decide what to work on when you have competing priorities?
Tell me about a professional failure and what you learned from it.
Describe a time you gave or received difficult feedback. What happened?

“I had a decent story ready for this but I rambled a bit trying to set up the context.”

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OpenAI·Software Engineer·Recruiter / HR Screen

May 2026

Screened for a software engineer role at OpenAI. Just one question from what I can tell, a self-intro prompt, which felt more like a recruiter warmup than anything technical.

Give a 1-2 minute self-introduction tailored to this role: cover who you are, your most recent role and team, a quantified example of impact, the types of problems you enjoy, and what you're looking for next and why it connects to this opportunity.

“I always overthink these.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Interviewed at OpenAI for what seemed like a research or applied ML engineering role. The question was a two-parter that went deep into probability theory and adversarial system design around LLM decoding. Pretty niche stuff, not your typical coding screen.

Given a per-step probability distribution over the vocabulary (including the EOS token), analyze the probability distribution of the stopping time during LLM decoding. That is, how many tokens get generated before an EOS or stop sequence is sampled?
Using your analysis of the stopping time distribution, design a decoding or early-stopping program that holds up against adversarial inputs. Consider adversaries that try to extend generation indefinitely, force premature stopping, or trigger a specific stop sequence. What guarantees can your program provide, and what does 'performing well' even mean here?

“This is basically a geometric distribution question at its core, but with a twist because stop sequences add memory to the process.”

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OpenAI·Machine Learning Engineer·Onsite - System Design / Architecture

May 2026

System design round at OpenAI for an ML Engineer role. The whole thing was basically one giant question about RAG systems, which sounds manageable until you realize they want you to cover everything from chunking strategy to access control to horizontal scaling in one coherent answer.

Design a full end-to-end RAG search system for enterprise users. Walk through the architecture, how data gets ingested, how you'd pick a retriever and generator, indexing strategy, latency constraints, security considerations, and how the system scales.

“This is a beast of a question and I underestimated how much the enterprise context changes things.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

Frontend system design round at OpenAI where they handed me a set of mockups and basically said 'build this in your head.' Covered a lot of ground fast, from component structure all the way to accessibility and build tooling. Felt like a senior-level bar even if the prompt sounded approachable at first.

Given a set of visual mockups, walk through how you would turn them into a working web product. Cover component decomposition, state management, data flow, API contracts, CSS strategy, accessibility, build tooling, and how you'd iterate from static to interactive.

“This is a wide question and I underestimated how wide.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026

Applied ML exercise at OpenAI for an MLE role, basically a live coding session where you're handed a noisy-labeled DataFrame and told to make the model better without peeking at the validation set. The interviewer peppered you with metric fundamentals while you were mid-code, which was a lot to juggle.

You're given a labeled training DataFrame with an annotator_id column. Annotator quality varies. How do you design and implement an approach to clean, relabel, or reweight the data to improve validation performance, without ever consulting the validation labels?
Before changing anything, what diagnostics do you run on the baseline model and the dataset itself?
How do you estimate which training examples are likely mislabeled without leaking information from the validation set?
If multiple annotators label the same item, how do you aggregate their labels beyond simple majority vote?
Walk through the E-step and M-step of a Dawid-Skene estimator. What breaks when most annotators agree on the wrong answer?
One annotator labeled only 12 examples and appears to have 100% accuracy. Why is that misleading, and how do you handle it?
An annotator systematically confuses two specific classes rather than flipping labels randomly. How does your noise model and your fix change?
Your cleaning improves macro-F1 but lowers overall accuracy. Do you ship it? And what if the validation set was labeled by the same noisy annotators?
Define precision, recall, and F1. How does class imbalance change how you interpret them, and when would you prefer macro vs. weighted averaging?

“This was the whole exercise, not just one question.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Got a coding question at OpenAI that looked simple on the surface but had a few gotchas worth thinking through. The problem was self-contained but the carry logic and StopIteration boundary conditions are where people slip up.

Implement an IPv4 iterator class in Python. Given a starting IP address like '192.168.0.1', the class should iterate through every subsequent IPv4 address up to '255.255.255.255', following the standard Python iterator protocol with __init__, __iter__, and __next__.

“My first instinct was to convert the dotted-quad to a single integer and just increment it, which actually works cleanly.”

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OpenAI·Machine Learning Engineer·Onsite - System Design / Architecture

May 2026

System design round at OpenAI for an ML Engineer role. The whole thing was one big open-ended prompt about building a managed LLM fine-tuning platform on AWS, and they wanted the full picture: architecture, APIs, training workflow, data handling, evaluation. A lot of ground to cover in one session.

Design a managed platform on AWS where customers can upload datasets, select a base model and fine-tuning method, launch and monitor training jobs, and deploy the resulting model to an inference endpoint.
How would you handle distributed training and checkpointing so that jobs can recover from failures without restarting from scratch?
What's your approach to reproducibility across training jobs, covering dataset versioning, code versioning, and job configuration?
How would you design cost controls and quota enforcement for a multi-tenant fine-tuning platform?
Walk through your observability strategy for training jobs, including metrics, logs, and traces.
How would you structure the evaluation strategy after fine-tuning, so users can assess whether their model actually improved?

“This is the core question and it's massive.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

OpenAI software engineer interview with a coding round focused on IP address iteration. The problem had a clean surface but the follow-ups pushed into bitwise territory pretty fast.

Design an iterator over a range of IPv4 addresses. It should support both a start/end pair and CIDR notation as input, expose next() and hasNext() methods, and handle large ranges without blowing up memory.

“Started with the CIDR case and immediately fumbled explaining how to extract the host range from the prefix length.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

System design round at OpenAI for a software engineer role. The whole thing was basically one giant chess platform question that kept expanding the more I answered, covering OOD, backend services, scaling, anti-cheat, the works. Left feeling like I'd done okay on the core design but got a bit lost toward the end on the scaling specifics.

Design the core object model for a chess game, including the board, piece hierarchy, players, moves, and game state.
How would you implement move validation, and handle check, checkmate, stalemate, castling, en passant, and pawn promotion?
How would you design the matchmaking service, including ELO-based pairing?
How would you synchronize moves in real time between two players, and what transport mechanism would you use?
How would you persist game state and allow players to resume interrupted games?
How would you implement time controls and per-player clocks in the system?
How would you detect cheating, specifically players using a chess engine to assist their moves?
How would you design support for spectators, chat, game replay, and rating history?
How would you scale this platform, covering sticky sessions, regional servers, and reconnection handling?

“Started with a Piece base class and subclasses per piece type, which felt clean.”

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