The notebook format threw me more than I expected.
Start by clarifying the task requirements and constraints, then outline a clear plan before coding. Implement a simple, correct solution first, then optimize or add complexity as time allows, explaining your reasoning throughout.
Pro tip: Narrate your thought process and trade-offs as you code; interviewers value clear communication and problem-solving over silent perfection. If stuck, simplify the problem and state assumptions.
Ask clarifying questions to understand the task scope, inputs, outputs, and constraints. Outline your approach and get buy-in before coding.
Import necessary libraries, create a small sample input, and verify your environment. Write a quick test case to validate your understanding.
Code a straightforward, correct solution first, even if not optimal. Focus on readability and modularity.
Run your code on the sample and edge cases. Debug any issues and verify correctness.
If time permits, improve efficiency or handle edge cases. Discuss alternative approaches and their trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope and constraints (e.g., time, resources, impact) to show product sense, then brainstorm 2-3 research ideas that align with OpenAI's mission and current challenges, and finally dive into one idea with a testable hypothesis and potential evaluation metrics. Emphasize feasibility, scalability, and alignment with OpenAI's values like safety and beneficial AGI.
Pro tip: Tie your ideas to OpenAI's existing work (e.g., RLHF, multimodal models, safety) and propose concrete next steps, showing you understand both the research and engineering trade-offs.
Ask clarifying questions to understand the context, such as whether the focus is on foundational research or applied product features, and what resources or timelines are involved.
Generate 2-3 high-level research directions that are open-ended but relevant to OpenAI's mission, such as improving model reasoning, safety, or efficiency.
Choose the most promising idea and refine it into a specific research question with a clear hypothesis, potential methods, and expected outcomes.
Outline how you would approach the research from an engineering perspective, including data, compute, evaluation metrics, and potential challenges.
Concisely recap your idea and its value, then invite the interviewer to discuss or suggest alternative directions, showing openness and adaptability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.