Anthropic
AI safety and research company behind Claude, focusing on constitutional AI.
5 Rounds
~20 Days
Very Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Data Scientist
•
Coding
•
hard
Given a dataset of prompt-response pairs with boolean safety violation flags from human annotators and a classifier's probability scores, write a script to compute the ROC-AUC score from scratch.
#Python
#ML Metrics
#Algorithms
Data Scientist
•
Technical
•
hard
How would you design a robust evaluation metric to measure hallucination rates in Claude's summarization tasks across different domains (e.g., legal, medical, casual)?
#LLM Evaluation
#Hallucination
#Metrics Design
Data Scientist
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Technical
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hard
From a data distribution and statistical perspective, explain the differences between preparing preference data for Direct Preference Optimization (DPO) versus traditional RLHF (PPO).
#RLHF
#DPO
#Preference Data
Data Scientist
•
Technical
•
hard
How do you detect and mitigate data contamination (test set leakage) in the massive pre-training corpus of a large language model to ensure our benchmark scores are valid?
#Data Contamination
#Test Leakage
#Pre-training Data
Data Scientist
•
Technical
•
hard
Describe how you would detect data contamination (test set leakage) in a massive 5-trillion token pre-training corpus.
#Data Quality
#NLP
#Algorithms
Data Scientist
•
Technical
•
medium
Explain the concept of Constitutional AI. How would you quantitatively measure if a model is adhering to its constitution?
#Constitutional AI
#Alignment
#Metrics
Data Scientist
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Technical
•
medium
What are the trade-offs between using automated LLM-as-a-judge evaluations versus human annotators for scoring model helpfulness?
#LLM Evaluation
#Bias
#Data Quality
Data Scientist
•
Technical
•
hard
How do you mitigate the 'length bias' (where models or humans prefer longer answers regardless of quality) in RLHF data?
#RLHF
#Bias Mitigation
#Modeling
Data Scientist
•
Technical
•
hard
Explain the difference between PPO (Proximal Policy Optimization) and DPO (Direct Preference Optimization) from a data requirements and modeling perspective.
#RLHF
#DPO
#PPO
Data Scientist
•
Technical
•
medium
How would you evaluate the coding capabilities of an LLM beyond just exact-match pass@k on standard datasets like HumanEval?
#Evaluation
#Code Generation
#Metrics
Data Scientist
•
Technical
•
hard
How would you design an evaluation metric to quantify the rate of subtle hallucinations in Claude's long-form summarization tasks?
#LLM Evaluation
#NLP
#Metrics Design
Data Scientist
•
Technical
•
hard
How would you measure the trade-off between helpfulness and harmlessness (the 'HHH' alignment) when evaluating a new model checkpoint?
#AI Safety
#Trade-off Analysis
#Experimentation
Data Scientist
•
Technical
•
medium
How would you detect and quantify data contamination (test set leakage) in our pre-training corpus?
#Data Processing
#NLP
#Model Evaluation
Data Scientist
•
Technical
•
hard
Explain the mathematics and intuition behind Proximal Policy Optimization (PPO) at a high level, and why it is preferred for RLHF.
#Reinforcement Learning
#Math
#RLHF
Data Scientist
•
Technical
•
medium
How do you handle severe class imbalance when training a classifier to detect rare jailbreak attempts in user prompts?
#Classification
#Imbalanced Data
#Security
Data Scientist
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Technical
•
hard
What are the primary limitations and biases of using strong LLMs as judges for evaluating the outputs of other LLMs?
#LLM Evaluation
#Bias
#Research Methodology
Data Scientist
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Technical
•
medium
Explain how you would cluster millions of unstructured user prompts to identify emerging use cases and feature requests.
#Unsupervised Learning
#NLP
#Clustering
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Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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