OpenAI Machine Learning Engineer Interview Questions
15+ questions from real OpenAI Machine Learning Engineer interviews, reported by candidates.
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I interviewed for an ML infrastructure position. The task was to design a mem allocator manager with a total capacity of N. It included functions like `allocate()` and `free()`. I initially implemente
The Challenge This is a system design question for Machine Learning Engineers. We do not have the full details, but here is the core task: * **Find novel data:** You need to extract new, unique inf
The Challenge You need to build a smart chatbot. This system uses Retrieval-Augmented Generation (RAG) to answer questions from users. Think of it like a business tool (such as Glean) that searches
Transformer Debug
The Challenge This question asks Machine Learning Engineers to fix a broken **Transformer** model. The code contains **4 bugs**. Comments in the file point out exactly where the mistakes are. Your
Vectorized 1-NN and Neural Network Forward Pass
Vectorized 1-NN and Neural Network Forward Pass ## Problem Overview This Machine Learning coding interview has been reported as a **two-part question**: 1. Implement **1-nearest-neighbor (1-NN)** us
Data Labeling Task Scheduler ## Problem Overview This OpenAI Machine Learning Engineer question was reported as a **two-part coding problem** about constructing a schedule for data labeling work. *
**Coding: GPU Credit Problem** **Problem Statement** The challenge involves managing GPU credits that have specific start times and expiration times. A common mistake is aggregating total credits firs
## Round 1 - MLE Coding ## Problem Implement the backward pass (backpropagation) for a single fully-connected layer with ReLU activation. You are given the gradient flowing in from the next layer and must compute the gradients with respect to the layer's weights, biases, and input. ```python import numpy as np def relu(x: np.ndarray) -> np.ndarray: ... def relu_grad(x: np.ndarray) -> np.ndarray: ... # 1 where x>0, else 0 def linear_backward( dout: np.ndarray, # gradient from upstream, shape (batch, out_features) x: np.ndarray, # input to this layer, shape (batch, in_features) W: np.ndarray, # weights, shape (in_features, out_features) b: np.ndarray, # biases, shape (out_features,) pre_activation: np.ndarray # Wx + b before ReLU, shape (batch, out_features) ) -> tuple: # Returns (dx, dW, db) ... ``` ``` Shapes check: dout: (32, 128) x: (32, 256) W: (256, 128) pre_activation: (32, 128) dx: (32, 256) <- gradient w.r.t. input dW: (256, 128) <- gradient w.r.t. weights db: (128,) <- gradient w.r.t. bias ``` ## Follow-ups 1. What does multiplying `dout` by `relu_grad(pre_activation)` represent in the chain rule? 2. Why is `dW = x.T @ delta` and not `delta @ x.T`? 3. How would the backward pass change for sigmoid activation instead of ReLU? 4. How would you numerically verify your gradient implementation?
## 75-min Coding Interview: Design a Data Structure Optimized for Reads and Writes Implement a **custom data structure** in a language of your choice that is optimized for both **reads** and **writes
## 60-min ML Coding Interview (recoverable from the prompt) You will work in an online notebook (requires a Google account). The interview includes: 1. **Math + coding tasks** related to machine lea
You are cleaning human annotation data. Given samples and their labels from one or multiple annotators (optionally with annotator IDs, confidences, timestamps, and a small gold set), design and implem
Solve a ML-based puzzle and implement it in code. Familiarity with vector and matrix addition/multiplication in numpy, common neural network layers, and implementation of linear layers with batched in
Debug a given machine learning model implemented using Python and PyTorch (a transformer model). Identify and fix all bugs to ensure the model runs successfully. Demonstrate knowledge of ML architectu
ML Coding
Solve a ML-based puzzle and implement it in code. It's useful to be familiar with numpy: adding and multiplying vectors and matrices, common neural network layers. Having a crisp understanding of how
ML Debugging
You are given a short implementation of a ML transformer model (using Python and PyTorch). Your task is to find and fix all bugs in order for the model to work successfully. This tests your knowledge
What OpenAI Looks for in Machine Learning Engineer Interviews
OpenAI Machine Learning Engineer interviews are calibrated against the level and scope expected of the role. Across 15+ verified candidate reports on LeakCode, the consistent signals interviewers look for: clear problem decomposition before coding, explicit complexity reasoning, structured handling of edge cases, and the ability to articulate trade-offs between two reasonable approaches.
The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the solution. It is the path to the solution: did you ask clarifying questions, did you state your approach before coding, did you handle edge cases without prompting, and did you communicate your reasoning throughout. Reports tagged "no hire" frequently cite a working solution with poor communication; reports tagged "strong hire" cite clear thinking even when the final solution was incomplete.
How To Use This Question Set
Real interview reports are a calibration tool, not a memorization target. Companies update their question pools every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage use: identify the patterns that appear repeatedly in OpenAI Machine Learning Engineer reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions below by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of OpenAI's pool. Reports tagged with quantified difficulty (e.g., "medium-hard") are higher-signal than reports without difficulty tags.
Round-by-Round Expectations
OpenAI Machine Learning Engineer loops typically span 4-6 rounds across phone screens and on-site or virtual on-site interviews. The structure varies by company: some run 1 recruiter screen + 1 technical phone + 3-4 on-site rounds; others run 1 recruiter screen + 1 OA + 4-5 on-site rounds. The recruiter screen is logistics and culture-light; the technical phone screen is medium-difficulty coding; the on-site loop covers coding, system design (at L4+ levels), and behavioral rounds.
Each round is designed to surface a specific signal. Coding rounds: correctness, code quality, complexity reasoning, communication. System design rounds: requirements clarification, design judgment, operational thinking. Behavioral rounds: ownership scope, leadership, ambiguity tolerance, conflict navigation. Strong candidates explicitly hit each signal dimension out loud during the round; weak candidates focus only on solving the prompt.
Common Interview Mistakes At This Combination
Reports tagged "no hire" at OpenAI Machine Learning Engineer commonly cite: jumping into code without clarifying requirements, coding silently for 10+ minutes without verbalizing approach, missing edge cases (empty input, single element, very large input, overflow), and producing a working solution that the candidate cannot explain or refactor when probed. Strong candidates avoid these patterns by following a consistent template: clarify, verbalize approach, code with narration, test with examples.
Behavioral and design rounds have their own failure modes. Behavioral: stories that use "we" instead of "I" diluting individual signal, stories with no quantified outcome, defensiveness when probed about failure. Design: not asking clarifying questions, not stating requirements out loud, designing for a single server when the prompt clearly implies scale, ignoring operational concerns (deployment, monitoring, rollback). These show up in roughly half of OpenAI Machine Learning Engineer interview retrospectives on LeakCode.
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