I interviewed with Anthropic for a senior/staff-level engineering role. Overall, the process was well structured, rigorous, and intellectually engaging. The team was professional throughout, and the interviews felt more focused on how I think through ambiguous technical and product problems than on memorized answers. The process included an initial recruiter conversation, followed by technical and leadership-oriented rounds. The recruiter screen covered my background, motivation for Anthropic, role fit, and expectations around scope, level, and team match. Anthropic’s candidate guidance also emphasizes authenticity and responsible AI use: AI tools can be useful for preparation and polishing communication, but assessments and live interviews are intended to evaluate the candidate’s own thinking. The technical rounds were challenging but fair. The questions were not purely LeetCode-style; they leaned more toward practical engineering judgment, system design, scalability, and tradeoff analysis. One technical discussion involved designing a routing or scheduling layer for serving requests across multiple backend systems, with attention to consistency, edge cases, failure modes, and performance. Another round focused on reasoning through a real-world engineering problem under incomplete information, including how to simplify the problem, identify bottlenecks, and communicate tradeoffs clearly. The behavioral and leadership rounds were also important. I was asked about past projects where I had led ambiguous technical work, influenced cross-functional stakeholders, handled disagreement, and made decisions under uncertainty. The interviewers cared about both technical depth and judgment: how I define success, how I prioritize safety and reliability, and how I work with teams when the right answer is not obvious. A few limited examples of topics discussed: Designing a reliable request-routing or model-serving system with sticky assignment, capacity constraints, and failure handling. Explaining a complex technical migration or architecture decision from a previous role. Discussing how to balance product velocity, reliability, and safety in an AI-related system. Reflecting on leadership style, communication, and how to operate in ambiguous environments. What stood out most was that Anthropic seemed to evaluate for taste and judgment, not just raw coding ability. The interviewers expected clear thinking, concise communication, and strong ownership. They also seemed to care deeply about whether candidates understand Anthropic’s mission and can reason seriously about the risks and benefits of advanced AI. Overall, I found the process demanding but thoughtful. Preparation should include system design, practical distributed systems concepts, examples of high-impact technical leadership, and a clear point of view on AI safety and responsible deployment. I would recommend candidates prepare real stories from their own work rather than generic interview answers. The bar felt high, but the process was respectful and gave me a good sense of the company’s engineering culture. Difficulty: High Experience: Positive Advice: Be ready to explain your thinking clearly, go deep on past work, and discuss tradeoffs rather than just presenting polished final answers.