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Choose a genuine but low-stakes work dislike (e.g., writing detailed documentation or manual QA) that won't undermine your fit for the role. Then, in the persuasion portion, acknowledge the shared dislike, reframe the work as a necessary evil that enables team success, and propose a concrete plan to minimize the pain for both of you. The key is to show empathy, pragmatism, and a willingness to roll up your sleeves when the team needs it.
Pro tip: Don't pick something that's core to the job (like coding) or that you'd refuse to do. Instead, pick a task that's universally disliked but clearly necessary, and show that you can separate personal preference from professional responsibility. This demonstrates maturity and adaptability.
Select a task that is genuinely unpopular but not central to your identity as an engineer, such as writing exhaustive test cases or updating legacy documentation. Avoid anything that suggests you'd avoid core responsibilities.
Validate the interviewer's dislike by agreeing that the task is tedious or frustrating. This builds rapport and shows you're not pretending to enjoy it.
Explain how doing this work benefits the team, the product, or the mission—e.g., it prevents bugs, unblocks others, or ensures compliance. Connect it to a higher purpose.
Suggest ways to make the task less painful, such as automating parts, rotating the duty, or time-boxing it. Show you're solution-oriented, not just resigned.
State clearly that you'll do the work when the team needs it, even if you dislike it, because you prioritize team success over personal preference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Choose a genuine but low-stakes example where you made a judgment error, focusing on the learning and systemic changes you made. Be honest about the mistake without being self-flagellating, and emphasize how you now proactively seek ethical guidance and consider broader impacts.
Pro tip: Show that you've internalized a framework for ethical decision-making, not just fixed a one-time mistake—this demonstrates maturity and alignment with Anthropic's safety-focused culture.
Briefly describe the situation and the specific action you took that you later realized was ethically or morally wrong. Be clear and concise, avoiding excessive detail that could overshadow the learning.
Describe the moment or process through which you became aware of the ethical problem. This could be through feedback, self-reflection, or observing consequences.
Explain what you did to address the situation: apologizing, mitigating harm, informing stakeholders, or changing the process. Show accountability and a focus on repair.
Articulate the concrete changes you made to your behavior, decision-making process, or team practices to prevent similar issues. Emphasize ongoing vigilance and learning.
Tie your experience to broader ethical principles or frameworks you now apply, such as considering downstream impacts, seeking diverse perspectives, or prioritizing user trust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.