Preparing for OpenAI Coding & Algorithms interview questions requires recognizing that OpenAI blends classic algorithmic rigor with product- and safety-oriented thinking. Interviews test algorithmic problem solving, data structures, time/space trade-offs, clean testable code, and the ability to reason about scaling, failure modes, and abuse vectors for real systems. Expect a multi-stage process—recruiter screen, one or two technical screens (pair coding or timed coding), and a final loop of 4–6 interviews covering coding, system design, deep-dives, and behavioral questions—lasting a few hours over one or two days. For interview preparation, practice medium-to-hard coding problems, timed pair-coding, system design scenarios relevant to model serving and data pipelines, and prepare concise STAR examples that highlight ownership and collaboration. Read OpenAI’s public materials and be ready to discuss trade-offs, testing, and safety considerations. Emphasize clear communication, test coverage, and justifying design choices; interviewers value well-reasoned, production-minded answers over trick solutions. Bring specific examples of shipping code and measurable impact.