Recently, one of my students successfully received an offer from Anthropic, a top AI company.This was not only a result of his personal efforts but also a reflection of our collaborative efforts in planning, overcoming challenges and systematic preparation. was not only the result of his personal efforts, but also a reflection of 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 your way to finding a job, especially if you are targeting a top tech company.
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
- Resume Review: The Recruiter will first look at the resume to make sure the 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
- Format: usually time-limited testing on platforms like HackerRank/CodeSignal.
- Content: 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 functions 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: Examines basic machine learning principles, common algorithms, and model tuning methods.
- Project Review: Discuss your past projects in depth, 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
Question: In a distributed dialog system, there are n server nodes that process user messages. Given a list of messages messages (each containing a user_id and content), design an algorithm that distributes messages with the same user_id as evenly as possible across the server_count nodes, returning An array of node indexes assignments corresponding to each message.
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
Question: Given a list of predictions, each containing a category and a confidence, as well as 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
Question: there are m annotation tasks, each containing difficulty and time (minutes); there are n annotators, each with max_ difficulty and max_time per day. difficulty and max_time per day. Design an assignment algorithm that maximizes the number of tasks completed per day and returns a two-dimensional array assignments, where assignments[i] is the indexed list of tasks assigned to the ith annotator.
tasks = [[2,10], [3,5], [1,8]]
annotators = [[3,15], [2,20]]Output:
[[0,1], [2]]
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