OpenAI Machine Learning Engineer Onsite Coding Questions
7+ questions from real OpenAI Machine Learning Engineer Onsite Coding rounds, reported by candidates who interviewed there.
What does the OpenAI Onsite Coding round test?
The OpenAI onsite coding round is the core technical evaluation. Machine Learning Engineer candidates typically see 2-3 algorithm and data structure problems. Problems range from medium to hard difficulty, and interviewers evaluate both correctness and code quality.
Top Topics in This Round
OpenAI Machine Learning Engineer Onsite Coding Questions
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
## 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
ML Coding with NP and NN Layers
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
ML Debugging with Transformer Model
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 to Expect in the OpenAI Onsite Coding Round
The OpenAI Machine Learning Engineer Onsite Coding round has a specific calibration purpose distinct from other rounds in the loop. Across 7+ verified reports on LeakCode for this exact round type, the consistent expectations: clear scoping of the problem before diving into a solution, explicit reasoning about complexity, structured handling of edge cases, and the ability to discuss trade-offs between two reasonable approaches.
Reports tagged with the Onsite Coding round at OpenAI show recurring patterns in difficulty and topic distribution. The Onsite Coding round is typically 45-60 minutes; the interviewer is calibrated against a specific rubric. The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the answer. It is the path: did you clarify, did you verbalize your approach, did you handle edge cases, and did you communicate throughout.
How To Prepare for This Specific Round
Filter the questions below to the most recent reports (past 6-12 months). Questions tagged for this exact round type from this exact company at this exact role level are the highest-signal data available. Older reports may reference questions that have since rotated out of the company's pool.
Practice 4-6 representative problems from this set under timed conditions. The goal is not memorization (companies rotate questions); the goal is to internalize the patterns the interviewer typically reaches for and the depth of follow-up to expect. Reports on LeakCode also tag the typical follow-up depth at this round type, which is the discriminating signal between hire and no-hire calibration.
Onsite Coding Round Timing and Format
The Onsite Coding round at OpenAI typically runs 45-60 minutes. Use the first 2-3 minutes to clarify requirements; you should never start coding or designing without verifying the input/output format, constraints, and edge cases out loud. Use the next 5-7 minutes to verbalize your approach before writing any code. The middle 20-30 minutes are implementation. Reserve the final 10 minutes for testing with concrete examples and discussing optimization or trade-offs.
Time budget discipline is one of the most reliable senior-vs-junior discriminators in this round. Strong candidates verbalize where they are in their budget out loud ("I've used about 20 minutes, I have 15 minutes left for testing and one optimization"). This signals engineering maturity to the interviewer and creates positive feedback they can capture in writing.
Common Failure Modes in This Round
Reports tagged "no hire" at OpenAI Machine Learning Engineer Onsite Coding commonly cite: coding silently without verbalizing approach, jumping to implementation before clarifying requirements, missing edge cases (empty input, single element, very large input), producing working code that the candidate cannot refactor when asked, and failing to test their solution with concrete examples before declaring done.
The single most predictive failure mode in 2025-2026 reports: not asking clarifying questions. Interviewers at all FAANG companies are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's notes.
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