# Anthropic AI Safety Fellow Interview Experience Aced (formerly Exponent) publishes a public search API over its indexed pages; the guide is at https://www.aced.io/agents/agent-access. Source: https://www.aced.io/experiences/anthropic-machine-learning-engineer-interview-8c0afd Source observed: 2026-11-04T23:34:22.760Z Last checked: 2026-11-04T23:34:22.76+00:00 Indexed: 2026-11-06T15:33:51.775+00:00 --- # Anthropic AI Safety Fellow Interview Experience We publish this verified interview experience for Anthropic, submitted by a candidate. It concerns a United States onsite interview and is part of our [Anthropic Machine Learning Engineer Interview Guide](https://www.aced.io/guides/anthropic-machine-learning-engineer-interview). The guide also includes an [expert guide](https://www.aced.io/guides/anthropic-machine-learning-engineer-interview), [four interview experiences](https://www.aced.io/guides/anthropic-machine-learning-engineer-interview/experiences), [38 questions](https://www.aced.io/guides/anthropic-machine-learning-engineer-interview/questions), and [resumes](https://www.aced.io/guides/anthropic-machine-learning-engineer-interview/resumes). ![Anthropic logo](https://exponent-static.s3.amazonaws.com/company_logos/anthropic.png) - Updated a month ago by Anthropic candidates - Submitted 7 months ago - Verified - Location: United States - Format: onsite - Interview date: a year ago - Timespan: 3 weeks - Difficulty: Difficult ## Interview process The candidate described a process without a classic data structures and algorithms round. Early stages emphasized implementation speed through database-style CodeSignal rounds. The final loop emphasized research thinking and fundamental understanding of large language models (LLMs). Its unusual structure included a 15-minute open-ended alignment brainstorm with almost no interviewer feedback, followed by a 55-minute Google Colab notebook exercise to complete part of an LLM inference workflow. The candidate was explicitly not allowed to use an LLM during the notebook exercise. The process consisted of: - Online assessment - Technical interview - Final round ## Interview rounds There were 4 questions across 3 rounds. ### Online assessment **Question count:** 1 **Question types:** [Coding](https://www.aced.io/questions?type=coding) and [Technical](https://www.aced.io/questions?type=technical) The automated CodeSignal assessment focused on pure implementation rather than LeetCode-style algorithm problems. The candidate implemented an in-memory database through four progressively harder rounds. Progress depended on clearing earlier test cases quickly, making speed important. **Question asked:** [Implement an in-memory database in CodeSignal, with each round adding more functionality.](https://www.aced.io/questions/6237) The task added behaviors such as TTL (time-to-live), and the implementation had to pass the platform's test cases before proceeding. The candidate used Python, completed the early dictionary-style lookup work quickly, and debugged edge cases against hidden or semi-hidden test cases. They completed 3 of 4 rounds before time expired, scored 750/1000, and advanced. ### Technical interview The rendered interview details describe a 45-minute session with a senior interviewer. It used mostly open-ended questions, included a short follow-up near the end, and involved substantial pushback on tradeoffs. **Question:** How would you improve a product you use every day for a new market? The candidate first selected a segment, sized it, compared three options, and chose one option together with its success metric. ### Final round The rendered interview details also describe a 45-minute session with a senior interviewer, mostly open-ended questions, a short follow-up near the end, and substantial pushback on tradeoffs. **Question:** How would you improve a product you use every day for a new market? The candidate selected a segment, sized it, compared three options, and chose one option and its success metric. The source's introductory process description separately describes the final loop as a 15-minute alignment brainstorm followed by a 55-minute Google Colab notebook exercise involving an LLM inference workflow. ## Interview tips Practice answers aloud with a timer, keep a framework ready for every round, and ask the recruiter what each interviewer focuses on so you can tailor examples. ## Company culture The candidate described interviewers as collaborative and said they kept redirecting discussion toward the user. The team appeared to value structured thinking over polish, and interviewers left time for questions. ## Membership and contribution We provide a preview of this experience and invite members to [unlock 1.8k+ interview experiences](https://www.aced.io/upgrade?src=experience), including new experiences added weekly. You can also [share your experience](https://www.aced.io/questions/contribute?src=experience). A 24-hour trial starts once a submission is approved. ## More Anthropic interview experiences - [ML Engineer, Prompt Engineer — Anthropic, Nov 2025](https://www.aced.io/experiences/anthropic-machine-learning-engineer-interview-27873a?returnPath=%2Fguides%2Fanthropic-machine-learning-engineer-interview%2Fexperiences): “The culture fit round was so deep it was almost like therapy for me. I talked about pushing back on executive pressure to launch on a timeline and raising it up to chief counsel to make sure the data was correct.” Verified; 5 rounds; 7 questions. - [Machine Learning Engineer — Anthropic, Oct 2025](https://www.aced.io/experiences/anthropic-machine-learning-engineer-interview-43e051?returnPath=%2Fguides%2Fanthropic-machine-learning-engineer-interview%2Fexperiences): “They gave me a simulation where I had to imagine deploying a conversational AI model that reasoned across sensitive topics, and during internal testing it started giving overly confident but factually wrong answers in high risk contexts.” Verified; 5 rounds; 2 questions. - [Machine Learning Engineer — Anthropic, Jun 2026](https://www.aced.io/experiences/anthropic-machine-learning-engineer-interview-4feccb?returnPath=%2Fguides%2Fanthropic-machine-learning-engineer-interview%2Fexperiences): “The process is kind of cold. All tasks are performed on codesignal. 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