OpenAI Machine Learning Interview Questions

A deep learning question on backpropagation when a network's weight matrices are split across several workers that can only share data by gathering. It tests matrix calculus, reasoning about which gradients stay local and which must be combined, and handling uneven or empty partitions.

Dive into foundational theoretical concepts underpinning modern transformer architectures and large language models. This set of conceptual questions examines computational bottlenecks, such as the quadratic time and memory complexity associated with self-attention mechanisms relative to sequence length and embedding dimensions. It is an essential review for engineers preparing for rigorous machine learning system design interviews. Access the complete conceptual overview, detailed explanations, and expert answers with a subscription.

Debug and optimize a miniature autoregressive transformer designed to generate sequential pixel data for digit imagery. This OpenAI interview task requires you to identify subtle implementation errors, improve training stability, and integrate a key-value cache mechanism to accelerate inference performance. It evaluates your practical familiarity with transformer architecture internals and efficient sequence generation strategies. The complete debugging guide, architectural breakdown, and verified code solution require a paid subscription.

Reported as an OpenAI interview task, this question centers on engineering a robust internal pipeline for model evaluation, covering inference restart policies, memory-constrained entropy calculations, and fair annotation assignment. It tests your capability to apply probability bounds, optimize memory footprints, and manage complex distributed resources reliably. To explore the full problem architecture, mathematical proofs, and complete solution code, a subscription is required.

An ML fundamentals coding question on training data labelled by people of uneven quality. It tests estimating each annotator's reliability without ground truth, turning that estimate into cleaner training labels, and measuring the effect on a downstream classifier.

Explore this challenging probability and mathematical modeling exercise recently featured in technical evaluations at OpenAI. The problem requires candidates to analyze the geometric distribution of particle trajectories emitted from a central point toward an infinite boundary. It tests your ability to apply statistical distributions, compute spatial intersections, and translate physical phenomena into rigorous mathematical formulations. Unlocking the complete problem statement, deep analytical breakdown, and optimized model solution requires an active subscription.

A machine learning debugging question on a small GPT-style language model with four injected faults. It asks you to find and repair problems in masking, head merging, positional handling and the training objective, then to turn the model into a binary classifier.

This applied statistics and distributed systems question, reported from OpenAI interviews, explores how networked nodes can compute collective metrics in the presence of transmission errors. You will analyze communication protocols for gathering measurements at a central aggregator while mitigating the impact of imprecise channels. The prompt evaluates your grasp of consensus concepts, statistical noise reduction, and fault-tolerant distributed algorithms. The full problem and model solution require a subscription.

A machine learning fundamentals question on the bounds of the cross-entropy loss, what train and test curves look like once a classifier overfits, and how to implement and interpret a binned calibration metric.

Build a classification loss from scratch in NumPy, combining per-sample masking with label smoothing so that ignored rows contribute nothing and targets are softened away from one-hot. Reported in OpenAI interviews, this exercise checks whether you can turn a loss definition into stable vectorised code rather than recite it. You will handle log-sum-exp overflow, the smoothing term's effect on the target distribution, and the question of what to return when every sample is masked out. The full problem statement and a verified reference implementation are available to subscribers.

Recreate the double-descent curve in the simplest possible setting: ordinary least squares on synthetic Gaussian data, sweeping the parameter count from well under the sample size to well over it. Asked at OpenAI, this task tests whether you can set up a clean experiment and then explain the test-error spike at the interpolation threshold instead of hand-waving at it. You will manage the under- and over-parameterised regimes, the role of noise, and why the minimum-norm solution recovers as capacity grows. Subscribers get the full problem text and the worked analysis.

Model when a language model stops generating, then play against an adversary who gets to choose the worst stopping behaviour for you. This two-part OpenAI question starts with the probability distribution over decoding length and expected token count, and then turns into a minimax decision problem solved with dynamic programming. You will connect a per-step stop probability to a full distribution, handle the forced stop at the length cap, and reason about optimal play on both sides. Access to the full problem and the complete derivation requires a subscription.