Recently, one of my students successfully received Anthropic（The offer from Anthropic (a top AI company) was not only a result of his personal efforts, but also our collaborative efforts in planning, overcoming challenges, and systematic preparation. Today, I'm compiling and sharing Anthropic's interview experience from this journey, in the hope that it will provide some inspiration for those of you who are on the job search path, especially targeting top tech companies.
Anthropic Interview Experience: From Confusion to Completion
Anthropic Interview Process
Anthropic's interviews are generally divided into multiple phases and are designed to comprehensively assess a candidate's technical skills, problem-solving mindset, and fit with the company's culture and values, particularly AI security and ethics.
1. Initial Screening
- Curriculum Vitae Review: Recruiters look at resumes first to make sure skills and experience match the job requirements.
- a face-to-face telephone interview: A short call to learn about your background, motivation, and initial knowledge of Anthropic.
2. Coding
- formality: Usually tested for a limited time on platforms like HackerRank/CodeSignal.
- element: Moderately difficult algorithmic and data structure questions focusing on coding implementation and debugging skills.
3. Technical Interviews (TIs)
Multiple rounds of in-depth technical discussions, content may include depending on the position:
- Algorithms and Coding: Solve algorithmic and data structure problems, or implement functionality related to real-world scenarios, where the interviewer focuses on problem solving, code quality and efficiency.
- System design: Design scalable, reliable system architectures that examine your ability to cope with large-scale AI applications.
- Machine Learning/AI Theory: Examine basic machine learning principles, common algorithms, and model tuning methods.
- Project Review: An in-depth discussion of your past projects, covering the background, your contributions, challenges encountered and solutions.
4. Behavioral interview (Behavioral & Cultural Fit)
- Find out about your work style, your ability to work in a team, your attitude to challenges, and whether it aligns with Anthropic's values.
5. Values Interview (VI)
- Delve into your understanding of AI safety and ethics and how you can incorporate these concepts into your daily work.
6. VO
- After passing the pre-session, you will enter a full day of online "onsite": more technical and behavioral interviews, which may include take-home assignments and debriefings.
Exclusive Anthropic Interview Questions
1. Optimization of intelligent dialogue system message distribution
theme: In a distributed dialog system, there are n A server node processes user messages. Given a list of messages messages(Each article contains user_id with the content), design algorithms to convert the same user_id The messages are distributed as evenly as possible to the server_count on each node, returning an array of node indexes corresponding to each message assignments.
messages = [
{"user_id": "u1", "content": "msg1"},...
{"user_id": "u2", "content": "msg2"},.
{"user_id": "u1", "content": "msg3"}
]
[0, 1, 1]
2. Calibration of AI model inference results
Topic: Given a list of predictions, each item contains category and confidence, and a confidence threshold threshold . For results with confidence below the threshold, set their category to "unknown" and return the modified list.
predictions = [
{"category": "cat", "confidence": 0.8},
{"category": "dog", "confidence": 0.4}
]
threshold = 0.6
Output:
[
{"category": "cat", "confidence": 0.8}, {"category": "cat", "confidence": 0.8}.
{"category": "unknown", "confidence": 0.4}
]
3. AI data annotation tasking
theme: There are m Each of the labeled tasks contains difficulty together with time(minutes); with n Each annotator has a max_difficulty and daily max_time. Please design allocation algorithms that maximize the number of tasks completed per day and return a two-dimensional array of assignmentswhich assignments[i] For the purposes of allocation to the first i Bit labeler's task index list.
tasks = [[2,10], [3,5], [1,8]]
annotators = [[3,15], [2,20]]Output:
[[0,1], [2]]
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