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
Machine Learning Engineer
•
Coding
•
hard
Implement multi-head self-attention from scratch using PyTorch, including an optional causal mask.
#PyTorch
#Transformers
#Attention Mechanism
Machine Learning Engineer
•
Coding
•
medium
Implement dropout during both the forward and backward pass from scratch using NumPy.
#NumPy
#Backpropagation
#Regularization
Machine Learning Engineer
•
Coding
•
medium
Write a PyTorch custom autograd function (subclassing torch.autograd.Function) for a novel activation function, implementing both forward and backward passes.
#PyTorch
#Autograd
#Calculus
Machine Learning Engineer
•
Technical
•
medium
Why do we use Layer Normalization instead of Batch Normalization in Transformer architectures?
#Normalization
#Transformers
#Math
Machine Learning Engineer
•
Technical
•
medium
Explain the vanishing gradient problem and demonstrate mathematically how residual connections (ResNets/Transformers) mitigate it.
#Backpropagation
#Gradients
#Architecture
Difficulty Radar
Based on recent AI-sourced data.
Meet Your Interviewers
The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
SimulateUnwritten Rules
Think Out Loud
Always explain your thought process before writing code or drawing architecture.