OpenAI Interview Questions — Page 30

OpenAI·Data Scientist·Take-home Assignment

May 2026

SQL take-home for a Data Scientist role at OpenAI. One question, pretty involved, centered on building an experiment metrics report from scratch. The problem was well-scoped but had enough edge cases to trip you up if you rushed.

Write a SQL query to measure the performance of a free-month promotion experiment. Given an experiment_users table and a subscription_events table, return one row per variant with counts for eligible users, signups within 30 days, paid conversions within 60 days, churn within 90 days of first paid start, and a churn rate column.

“The churn definition is what got me.”

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OpenAI·Product Manager·Onsite - Product Sense / Strategy

May 2026

Interviewed for a PM role at OpenAI, got a product sense question about improving their flagship product. Pretty open-ended, which sounds fun until you're actually sitting there trying to say something that doesn't sound like every other PM answer they've heard.

How would you improve ChatGPT?

“I went straight to user pain points, talked about memory limitations and multi-modal consistency, then tried to tie it back to retention metrics.”

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OpenAI·Backend Engineer·Technical Phone Screen

May 2026

Got a coding question from OpenAI for a backend role that was more IP networking than I expected. One question, fairly self-contained, but the edge cases are where it gets you.

Write a function that takes a starting IPv4 address and a count, then returns the smallest list of CIDR blocks that covers exactly that many consecutive addresses starting from the given IP, no more and no less.

“The core idea clicked pretty fast: convert the IP to a 32-bit int, then greedily pick the largest valid aligned block that fits within the remaining count.”

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OpenAI·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Interviewed for a software engineer role at OpenAI. One behavioral question but the interviewer kept drilling into follow-ups, so it ended up feeling way longer than expected.

Walk me through the most challenging project you've worked on.

“The base question is fine, I had an answer ready.”

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

May 2026

OpenAI MLE interview focused entirely on designing a ChatGPT Enterprise system using RAG, and it went deeper than I expected on basically every component. The interviewer wasn't satisfied with surface-level answers and kept pushing on trade-offs I hadn't fully thought through.

Design a ChatGPT Enterprise system where companies upload internal data and get a customized chatbot that answers questions grounded in that data.
Walk through your chunking strategy for large documents. What chunk size would you use and how do you handle metadata?
How would you handle multi-tenancy isolation in the vector database? Tenant-scoped index vs shared index with filters?
Compare pointwise, pairwise, and listwise reranking. When would you choose each?
How do you prevent hallucinations in the LLM's responses, and how do you surface source citations to the user?
What evaluation metrics would you use to measure both retrieval quality and end-to-end answer quality?
How do you manage conversation history across multiple turns without blowing up the context window?
How would you reduce latency in the retrieval pipeline at scale?

“This is the main question and it's deceptively large.”

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OpenAI·Backend Engineer·Technical Phone Screen

May 2026

Interviewed for a backend role at OpenAI and got a classic encode/decode string problem. Nothing flashy, but it required more thought than I initially gave it credit for.

Design an encode function that serializes a list of strings into a single string, and a decode function that reconstructs the original list. The strings can contain any ASCII character, so you can't rely on a simple delimiter.

“My first instinct was to just join with a comma and I almost said it out loud before catching myself.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Interviewed for a SWE role at OpenAI and got a problem themed around infectious disease spread on a network, but the details were vague enough that I'm still not totally sure what they were actually asking for.

Given a network of cities and contacts, along with some initially infected nodes, simulate or compute metrics related to how an infectious disease spreads through the network.

“The problem was loosely framed around disease spread on a graph, which sounds interesting, but the actual inputs, outputs, and rules were never clearly defined.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026

Round 2 at OpenAI for an MLE role was a one-hour debugging session focused on transformers and PyTorch. Pretty hands-on, no fluff, just code.

You're given a broken miniGPT implementation in PyTorch. Find and fix the bugs so the model generates correct text.
Now implement a KV cache for the transformer you just debugged.

“This one had been floating around on forums so I'd seen a version of it before, which helped.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

OpenAI software engineer interview with a meaty algorithmic simulation problem. The question had multiple layers and I definitely underestimated how far they wanted me to take it.

You're given two teams of monsters, each with HP and attack values. Simulate a turn-based battle where monsters exchange damage simultaneously each turn, a defeated monster is replaced by the next in line, and each side can revive one monster per battle at full HP. Start with the basic case: pick one monster from each side, simulate the duel, and return the survivor with remaining HP.
Extend the simulation to find the optimal strategy for team A: search over all possible monster orderings and revival decisions to maximize the number of A's monsters that survive. Implement this using memoized DP with the state being the remaining monster sets for both sides, current active HP values, and whether each side has used their revival. Also discuss the state space size, how you'd key the memo table, and any pruning opportunities.

“The basic version felt like a warmup but I still spent too long on edge cases around simultaneous kills.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

OpenAI Research Engineer system design round, one big question that basically ate the whole session. They took an existing text editor spec and asked you to bolt on real-time collaboration, which sounds scoped until you realize they want the full distributed systems treatment.

You're given a text editor that already supports insert/delete operations, undo/redo, and prefix autocomplete with word frequency rankings. How would you extend it to support real-time collaborative editing across multiple clients, where operations can arrive out of order and all clients must eventually converge to the same document state?

“This one spiraled fast.”

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