Anthropic Machine Learning Engineer Interview Questions
6+ Anthropic Machine Learning Engineer interview questions drawn from real candidate reports. Sources include 1Point3Acres, Blind, Glassdoor, Reddit, and LeetCode. Questions span every stage of the Anthropic Machine Learning Engineer loop: OA, phone screen, system design, behavioral, and onsite coding.
What to Expect in the Anthropic Machine Learning Engineer Interview
The Anthropic Machine Learning Engineer interview process typically runs 4 to 6 rounds depending on seniority level. Based on candidate reports in the LeakCode database, the loop usually includes a resume review, an online assessment or coding phone screen, one or more technical rounds, a system design round (for senior and above), and a behavioral or values round.
Difficulty skews toward medium and hard LeetCode-style problems in the coding rounds. System design questions test breadth (component selection, scaling, trade-off reasoning) more than deep implementation. Behavioral questions are tied to the company's stated values and principles.
Anthropic Machine Learning Engineer Questions (Sample)
anthropic fulltime machine learning tech phone screen with coding & design
店面选了 Coding & Design: This interview will be a coding and design exercise involving writing code in Python. You should be comfortable with Python's standard library. 大家知道这个跟Coding是不是同一个类型？看地里的经验Coding基本是stack, image, crawler这类题。但是Coding & Design有人面到的data batcher更少见一些 面完再来这里汇报
anthropic fellows two-round codesignal online assessment review for machine learning intern
分享一下 人类学Fellows 的两轮 CodeSignal 面经，希望对后面申请的人有帮助 四月中申请的前几天收到， 第一关做完后马上收到第二关于是接着给他做完了 具体time line 如下 然后都是proctored开摄像头麦克风 以下内容需要积分高于 188 您已经可以浏览 第一轮是 90 分钟的 general coding assessment，我遇到的是一个偏系统/网络模拟的 Python 题，大方向是实现一个简化版的 DNS resolver。题目会分成多个 step，每一步对应一组 unit tests，需要逐步实现功能。整体不是传统 LeetCode 那种纯算法题，更像是给你一个 mini codebase 和清晰 spec，然后要求你按测试一步步补全逻辑。 难度体感上，我觉得第一轮大概是 LeetCode Medium 左右，但不是算法难，而是工程逻辑和边界处理比较多。前面几步比较 straightforward，比如字符串 normalize、基本递归/迭代查询；后面会逐渐加入更多情况，例如 alias / fallback / error hand...
anthropic fellows full-time machine learning tech phone screen interview experience and timeline
5.8 收到第一个codesignal OA, DNS resolver, 5天内做完 5.12 做完第一个oa, 500分左右out of 600，自动立马收到第二个oa, python debug, 同时收到邮件说给我的推荐人发邮件request references了，5天内做完oa, references 7天内得填完 5.16 做完第二个oa，做的时候codesignal出了点bug，对界面也不太熟所以一直很不方便debug，最后只得到了480/600左右的score，焦虑担心了很久，建议还是一定要多熟悉一下codesignal 5.24 收到5 hour take-home takeaway就是竟然真的像他邮件里说的一样oa不需要做到满分也能move forward，不过怀疑他最后final review这些oa的分数也会有影响？ 前两个oa这个帖子讲的很清楚了 https://www.1point3acres.com/bbs/thread-1177056-1-1.html 求大米！！同时求anthropic fe...
anthropic fulltime machine learning tech phone screen: coding and design questions
有人知道除了以下这些之外，还有哪些 coding and design 的题吗？ - uber (https://www.1point3acres.com/bbs/thread-1065604-1-1.html) - weighted data batcher (https://www.1point3acres.com/interview/problems/company/anthropic/coding-design-data-batcher) 另外，有人知道如果是在 CodeSignal 环境里，跟在 Collab 环境里，题会不会不一样啊？
Anthropic Interview Experience: 55-Minute Google Meet Coding Challenge on LLM Prompting
Hi everyone! I'd like to ask if anyone has experienced this type of interview from Anthropic: "A 55-minute coding challenge on prompting and engineering with LLMs in Colab" This is essentially a 55-mi
Anthropic Fulltime Machine Learning Engineering HR Screen Assignment
Has anyone received a similar assignment? It requires two hours to complete, involving debugging kernel/assembly/compiler code on a Python emulator for performance tuning.
Interview Rounds
Here is how the Anthropic Machine Learning Engineer questions in the LeakCode database break down by interview round, based on what candidates reported:
| Round | Questions in Database |
|---|---|
| phone screen | 3 |
| take home | 1 |
| recruiter | 1 |
| oa | 1 |
Most Common Topics
Question Recency
4
2026
2
2025
Question counts by interview year, based on candidate-reported dates.
How to Prepare for the Anthropic Machine Learning Engineer Interview
Use the LeakCode question database as your primary research tool. Filter by role (Machine Learning Engineer), then by round type to focus your prep on the specific stages in your upcoming loop. Sort by recency to see what 2026 candidates actually faced.
- Start with questions from the last 12 months. Interview processes change and recent data is the strongest signal.
- Cross-reference questions that appear in multiple sources (1p3a, Blind, Glassdoor). Multi-source confirmation means a question has stronger recurrence probability.
- For system design rounds: focus on the question patterns, not individual questions. The same design principles recur across many prompts.
- For behavioral rounds: map your experiences to the company's stated values before the interview. Most behavioral questions at top companies are derivatives of a small set of core leadership competencies.
FAQ
How many Anthropic Machine Learning Engineer questions are in the database?
6+ questions from verified candidate reports. The count grows as new reports are scraped daily from 1Point3Acres, Blind, Glassdoor, Reddit, and LeetCode.
Are these questions from real Anthropic interviews?
Yes. All questions are sourced from actual candidate interview reports, not generated by AI. Each entry links back to its source URL where available, and questions are tagged with the year and round reported by the candidate.
How current is this data?
LeakCode updates daily. The database is filtered to exclude duplicate and low-quality entries. You can filter by interview year to focus on recent cycles.
Does LeakCode cover Anthropic OA questions specifically?
Yes. The database includes online assessment questions tagged with round type. See the Anthropic OA page for a dedicated view.
Related: Anthropic All Questions · Anthropic OA Questions · Browse All Companies · Data Sources