OpenAI Interview Questions — Page 11

OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

Did a coding round for a Software Engineer role at OpenAI and got tripped up by a question I'd skipped during prep. The follow-up is what really exposed the gap in my thinking.

Refactor a chatroom system.
How would you redesign the system so that a bot only has access to a subset of messages rather than everything?

“I skipped this one in my prep because it showed up in older posts and I figured it was stale.”

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OpenAI·Machine Learning Engineer·Onsite - Behavioral / Leadership

Apr 2026

Interviewed for an ML Engineer role at OpenAI and the behavioral round was heavier on mission and values than I expected. Less about past projects, more about whether you actually buy into what they're building.

Why do you want to join OpenAI specifically, rather than somewhere like Google or Anthropic?
What do you think the right safety boundaries are for AGI, and where do you personally draw the line?

“This one tripped me up more than it should have.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

Technical screen for a software engineering role at OpenAI that went deep into neural network math fast. The whole session was basically one long question about backprop from scratch, no libraries allowed, and they wanted working code plus an explanation of the linear algebra.

Derive backpropagation for a two-layer network (Affine → ReLU → Affine → softmax), implement the forward pass and analytical gradients in NumPy without autograd, verify them with finite differences, and explain tensor shapes and the chain rule at each step.

“This was the whole interview.”

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OpenAI·Data Scientist·Technical Phone Screen

Apr 2026

Brutal technical screen for a DS role at OpenAI. One question, but it was basically a full experiment design thesis spanning seven sub-parts. I've done A/B testing questions before but nothing close to this level of detail.

Design a complete end-to-end A/B test to evaluate whether a 1-month free trial increases paid subscription conversions. Cover eligibility and randomization, the primary metric and observation window, intent-to-treat vs triggered analysis, sample size calculation, bias controls, fraud/interference handling, and your launch decision framework.

“This was a lot.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Interviewed for an ML Engineer role at OpenAI and got hit with a multi-part simulation problem that kept escalating in complexity. Each part built on the last and they wanted complexity analysis after every step, which I was not fully prepared for.

Simulate the spread of a virus across a grid of plants over discrete time steps. Each cell is either healthy or infected. A healthy cell becomes infected the next day if more than T of its 8 neighbors are currently infected. Write the update rule and analyze time and space complexity per day.
Extend the simulation to compute how many days it takes until the epidemic ends, meaning all plants are infected. What changes in your implementation and complexity analysis?
Add an immune state to the grid. Some cells can never be infected regardless of their neighbors. How does this change the update rule and the termination condition?
Add a self-healing mechanic: an infected cell heals after T consecutive infected days. The simulation ends when all cells are healed. Handle re-infection carefully and explain your update rule and complexity.
Propose and implement an open-ended extension to the simulation, such as heterogeneous infection thresholds per cell or stochastic transmission. Explain your design choices and analyze the impact on complexity.

“Felt okay about this part.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

Got a coding screen at OpenAI that was pretty much just one problem: build a reverse IPv4 iterator in Python. Straightforward on the surface but the borrow logic across octets tripped me up more than I expected.

Implement a reverse IPv4 iterator in Python. Given a starting IP string like '192.168.0.255', the iterator should yield every IPv4 address in decreasing order down to 0.0.0.0, using the standard __init__, __iter__, and __next__ protocol with correct StopIteration semantics.

“The parsing part was fine, split on dots, done.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Apr 2026

OpenAI ML Engineer interview with a noisy data / annotation quality problem. The format was hands-on: train a baseline, then figure out your own approach to clean things up. Lots of interspersed fundamentals questions along the way.

You're given a labeled dataset where annotator quality varies. Train a baseline model first, then come up with your own method to clean the noisy labels and improve performance.
What are precision, recall, and F1, and when would you prioritize one over the others?

“The open-endedness here is the whole point and also the most stressful part.”

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

Apr 2026

Brutal system design round at OpenAI for an MLE role. The whole thing was one long deep-dive into building an enterprise RAG product, and they kept pulling the thread on every answer I gave. Left feeling like I'd covered maybe 70% of what they wanted.

How would you design the document ingestion and chunking pipeline for an enterprise LLM assistant that needs to handle wikis, tickets, source code, and PDFs?
How do you extract metadata from ingested documents and use it to enforce access-control filtering at query time?
Walk me through how you'd implement hybrid retrieval combining dense vector search and sparse BM25 in a multi-tenant environment while keeping tenant data isolated.
Describe the three-stage retrieval and ranking pipeline you'd use, from initial candidate generation through reranking to final answer generation.
Compare pointwise, pairwise, and listwise reranking approaches. What are the specific drawbacks of pointwise reranking?
What metrics would you use to evaluate this system, covering both retrieval quality and end-to-end answer quality?

“I started with fixed-size chunking and they immediately pushed back.”

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OpenAI·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

Product sense interview at OpenAI focused on launching a new ChatGPT model. The questions got into cross-functional friction pretty fast, especially around finance, which I wasn't fully prepared for.

How would you approach launching a new ChatGPT model?
How would you handle pushback from finance or legal teams during the model rollout?
If the projected cost to run the new model over two years is $2 trillion, how do you present that number to finance?

“I started with phased rollout, safety evals, internal dogfooding, then public release.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at OpenAI focused on a classic distributed systems problem. The question felt deceptively simple at first but the concurrent duplicate request angle is where things got interesting.

A client accidentally sends the same network request more than once, due to a double-click, a retry after timeout, or network-level duplication. Design a system to prevent the operation from being processed twice, covering both client-side and server-side approaches, and explain how your design handles duplicate requests arriving at the same time.

“I started on the client side because that felt easier, debouncing the button, disabling it after the first click, that kind of thing.”

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