ML Model Debugging

Tech Deep DiveOpenAILast reported July 2025High Frequency
Reported
11× across candidate reports
First seen
January 2025
Last reported
July 2025
Reported outcome
mixed

Problem Overview

A dedicated ML debugging round in OpenAI onsite interviews (MLE and Research roles). Candidates are given existing ML code containing intentional bugs — typically 4–5 — with inline comments marking which sections contain bugs. The most common variant is a basic multi-head transformer implementation. Confirmed bug categories include: (1) sigmoid/activation…

  • 4 candidate-reported follow-ups — the exact probes interviewers asked, with the trigger for each
  • The rest of the problem statement — full requirements, constraints, and edge cases
  • Approach and trade-offs — what passing candidates did, and the mistakes that sink people
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Verified Source
Every question is reconstructed from multiple independent candidate reports. Verbatim follow-ups, not invented ones.
Codex Fact-Checked
Technical claims, formulas, and scale numbers are reviewed against primary sources.
Interviewer Follow-ups
The exact follow-ups reported by candidates, with the trigger that prompts each one — plus the mistakes that sink people.
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