Behavioral: why OpenAI, AI safety views, 0-to-1 work, feedback, defining success

These behavioral questions are reported from OpenAI's software engineering interview loop. The recruiter call asks how you think about AGI and AI safety. In the behavioral round and the hiring-manager conversation, "Why OpenAI?" and AI safety are the top questions, followed by your 0-to-1 experience, negative feedback you have received, and how you defined success for a project. A separate project deep dive has you present a project with slides and challenges your technical depth again and again. Weak answers to the motivation and AI safety questions are reported to end the process, and the behavioral answers are reported to weigh on the level offered. ### Clarifying Questions - For the deep dive, should the project be one you led end to end, or can it be a team project where you owned one component? - Is the AI safety question looking for your personal view, familiarity with the main debates in the field, or both? - How long should each behavioral answer run before the interviewer starts probing? ### Part 1 — Why OpenAI, and how you think about AGI and AI safety "Why do you want to work at OpenAI? How do you think about AGI and AI safety?" ```hint Make it yours Tie your motivation to something specific in your own work. Treat safety as a set of concrete risks and trade-offs, not a slogan. ``` #### What This Part Should Cover - Motivation that is specific to the company and connected to the role - Concrete risks and trade-offs, rather than generic statements - Calibrated uncertainty about questions that are genuinely open - How your own engineering work relates to safety ### Part 2 — A 0-to-1 project "Tell me about something you built from zero to one." ```hint Show the ambiguity Pick a project that started without a clear specification, and be ready to explain what you deliberately chose not to build. ``` #### What This Part Should Cover - The ambiguity at the start, and how it was resolved - The decisions you personally made - Iteration driven by evidence from users or data - A measurable outcome ### Part 3 — Negative feedback "Tell me about negative feedback you have received." ```hint Make it real Choose feedback that was hard to hear and that led to a change someone else could observe. ``` #### What This Part Should Cover - Feedback about something that mattered, not a disguised strength - How you reacted, and how you worked to understand the feedback - The concrete change you made, and evidence that it stuck ### Part 4 — Defining success "How did you define success for a project you worked on?" ```hint Before, not after Be ready to say when the success criteria were set, and what you would have done if the metric had moved the wrong way. ``` #### What This Part Should Cover - The user or business goal, and the metric chosen to represent it - Guardrail metrics, the baseline and the target - How the result was measured, and an honest account of the outcome ### Part 5 — Project deep dive Present a project you worked on, using about three or four slides. Expect to be interrupted and challenged on technical depth repeatedly. ```hint Prepare the second "why" For each design decision on your slides, prepare the alternative you rejected and the evidence that decided it. ``` #### What This Part Should Cover - Problem, context and your role, set out quickly - Key design decisions together with the alternatives you rejected - Depth under challenge: numbers, failure modes and production behavior - Results and an honest retrospective ### What a Strong Answer Covers - Specific, first-hand stories in which your own role is clear - Calibrated, concrete thinking about AI risk rather than slogans - Scope and ownership that match the level you are targeting - Composure and directness under interruption and repeated challenge - Honest treatment of mistakes, mixed results and uncertainty ### Follow-up Questions - Describe a time you disagreed with a decision because of a safety or reliability concern. What did you do? - If you started your 0-to-1 project again tomorrow, what would you do differently? - In your deep-dive project, which production failure taught you the most, and what did you change because of it?