OpenAI Machine Learning Engineer Interview Questions

OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Interviewed for an ML Engineer role at OpenAI and got hit with a pretty involved algorithmic problem about version strings and a rate-limited API. Two-part question that escalated fast, and the second part especially required some real design thinking under pressure.

You have a list of version strings in the format {major}.{minor}.{patch} with zero-padded segments. Given an arbitrary list of these versions and an isSupported(version) API call, return the smallest version (by version ordering) that supports a feature. Support is not monotonic. How do you parse and compare versions, handle duplicates or invalid inputs, and what is the time and space complexity?
Now the isSupported API is rate-limited so you can't make O(N) calls. Support is still not globally monotonic, but you're guaranteed that for any supporting version, support will eventually reappear in the next patch group, the next minor group, and the next major group. Design a sub-linear algorithm to find the earliest supporting version. Walk through correctness, call count, and edge cases like no supporting version at all or very sparse support.

“My first instinct was just sort and scan, which is correct but I fumbled explaining the comparison logic for zero-padded segments out loud.”

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

Jul 2026

System design round at OpenAI for an ML Engineer role, centered entirely on one meaty question about building a content moderation pipeline for harmful videos. No behavioral fluff, just a deep technical dive that covered a lot of ground fast.

Design a complete system to detect and moderate harmful videos on a large platform, covering multiple violation categories, both upload-time and retroactive scanning, a mix of automated and human review, low false positives and false negatives, and support for appeals and audit trails.

“This question is massive.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026

OpenAI ML Engineer interview with a meaty open-ended problem about noisy multi-annotator labels for binary text classification. One question but it sprawled into five or six sub-problems across analysis, training strategy, and evaluation design. Felt more like a whiteboard design session than a traditional interview.

You have a text dataset for binary classification where each example was labeled by multiple annotators who often disagree. Walk through how you'd analyze the label noise, build train/val/test splits, convert the multi-annotator labels into training targets, choose a model and loss function, and identify ways your offline metrics could be misleading.

“This question wrecked me a little because I kept trying to answer each sub-part sequentially and the interviewer just kept layering more on top.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026

OpenAI MLE interview centered on a machine learning problem about handling noisy annotators in a training pipeline. The format varied by round but the core technical content stayed consistent, and the emphasis was less on fancy methods and more on code structure and how you reason through tradeoffs.

You have a classifier trained on labels from multiple human annotators, some of whom are noisy. How do you identify the bad annotators, remove their labels, and verify whether the classifier actually improves?
Given a 400+ line PyTorch file implementing this classifier pipeline, walk through the code, identify structural issues, and refactor it into cleaner components.
How would you handle the bias-variance tradeoff when filtering annotator labels, given that the input dimensionality is high and you can't afford to lose too much data?

“This is the main question and it sounds straightforward until you actually have to code it up.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Interviewed for an ML Engineer role at OpenAI and got hit with a grid simulation problem that felt more like a competitive programming warmup than anything ML-related. Interesting problem but I kept second-guessing my BFS approach the whole time.

Given an n x m grid where cells are either infected or healthy, and infection spreads in discrete time steps (a healthy cell becomes infected if it has at least K infected neighbors), return the minimum number of time steps until the entire grid is infected, or -1 if it's impossible.

“My first instinct was multi-source BFS and that direction was right, but K not being 1 threw me off.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Went through a technical phone screen for an ML Engineer role at OpenAI that was pretty heavy on statistics and Bayesian inference. The whole thing felt like a grad-level probability exam more than a typical ML interview, which I wasn't fully expecting.

Given n Bernoulli trials with k successes, derive the maximum likelihood estimator for the success probability p and describe its asymptotic variance.
With a Beta(alpha, beta) prior on p, derive the posterior distribution and compute the posterior predictive probability of success on the next trial.
Construct a 95% confidence interval for p using a normal approximation, and separately a 95% credible interval from the posterior. Walk through both.
When is each type of interval actually reliable, and how does sample size change the picture for both frequentist and Bayesian inference?

“The MLE derivation itself is straightforward, k/n, anyone who's taken a stats course gets there.”

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

Jul 2026

System design round at OpenAI for an MLE role. The whole thing was a single deep-dive into building a production RAG system end-to-end, with follow-ups that kept branching into trickier territory. Felt more like a 45-minute architecture review than a standard interview.

Design the end-to-end architecture of a RAG system for question answering over a large internal document corpus, covering ingestion, chunking, embedding, retrieval, reranking, and generation with citations.
Why does pure dense vector retrieval fall short for certain query types, and how would you combine it with lexical search to improve retrieval quality?
How would you reduce hallucinations and ensure the generated answers are grounded in the retrieved source documents?
How would you handle documents with strict access controls so users can't see results from documents they're not permitted to access?
Walk through how you'd debug a case where the system confidently cited the wrong document. How do you determine whether the failure is in retrieval or generation?
Your embedding model gets upgraded to a new version. How do you re-index without creating a broken mixed-embedding-space index during the transition?
How would you extend the system to answer multi-hop questions that require combining facts from multiple documents?
The corpus has many near-duplicate documents. How does that affect retrieval quality and what would you do about it?
Describe your evaluation plan for a RAG system, covering both offline metrics and online monitoring, including how you'd detect corpus or embedding drift over time.

“This was the core question and it ate up most of the session.”

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

Jul 2026

System design round at OpenAI for an ML Engineer role, focused entirely on building a RAG-based search and QA system over a large mixed-format document corpus. The whole session was basically one giant design problem with follow-ups layered on top, which felt more like a technical deep-dive than a typical interview.

Design an LLM-powered search and question-answering system over a large corpus of documents including internal wikis, PDFs, logs, contracts, and web pages. Both documents and user queries can exceed the model's context window.
How do you handle documents that are hundreds of pages long and far exceed what you can fit into the context window?
How would you process a long user query, like a multi-page pasted log or contract, without losing the user's original intent?
How would you evaluate whether your chunking strategy is causing missed answers?
How would you handle questions that require synthesizing information across many documents rather than finding a single fact?
How do you handle permissioned documents in retrieval and caching without leaking data across users or teams?
What would change in your design if the task were summarizing an entire 200-page document rather than answering search queries over a corpus?

“This is the main question and it's a lot.”

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OpenAI·Machine Learning Engineer·Onsite - Coding / Algorithms

Jul 2026

ML coding round at OpenAI for an MLE role. The whole thing centered on one meaty calibration problem and then a discussion portion that went places I wasn't fully ready for.

Implement a calibration utility for LLM outputs: given logit/probability and ground-truth label pairs from a held-out set, fit a calibration map using temperature scaling, Platt scaling, or isotonic regression so that reported confidence matches empirical accuracy. Then write a function that takes a new logit vector and returns a calibrated probability distribution.
Walk through how you'd construct a reliability diagram and compute expected calibration error (ECE), and discuss the trade-offs between temperature scaling, Platt scaling, and isotonic regression as calibration methods.

“I went straight to temperature scaling because it's the simplest and I could actually derive the optimization cleanly under time pressure.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Interviewed for an ML Engineer role at OpenAI and got hit with a systems-flavored coding problem that I wasn't expecting to go as deep as it did. The dependency resolution angle made it feel more like a compiler or package manager design question than a pure algo problem.

Given a list of packages where each package has a set of dependencies (potentially with version ranges), write an algorithm to find a valid installation order or determine that no valid order exists. Be prepared to discuss how you'd handle dependency cycles, diamond dependencies, and version constraint conflicts.

“I started with topological sort which felt right, but then they kept pushing on version ranges and I realized I'd basically been ignoring half the problem.”

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