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
•
System Design
•
hard
Design a distributed training system for a 100B+ parameter language model. How would you partition the model across GPUs using tensor, pipeline, and data parallelism?
#Distributed Training
#3D Parallelism
#GPU Architecture
#Megatron-LM
Machine Learning Engineer
•
System Design
•
medium
Design an inference API for a large language model. Focus specifically on how you would handle continuous batching and manage the KV-cache efficiently to maximize throughput.
#Inference
#Continuous Batching
#KV Cache
#PagedAttention
Machine Learning Engineer
•
System Design
•
hard
Design a reward modeling pipeline to penalize evasive answers (e.g., 'As an AI...') while maintaining the model's helpfulness and harmlessness.
#Reward Modeling
#Alignment
#Data Pipeline
Machine Learning Engineer
•
System Design
•
hard
Design a distributed training system for a 100B+ parameter model across 1000 GPUs. How do you handle network topology and parallelism strategies?
#Distributed Training
#Networking
#Parallelism
Machine Learning Engineer
•
System Design
•
hard
Design an inference API for a model like Claude that handles high concurrency, minimizes Time to First Token (TTFT), and maximizes throughput.
#API Design
#Inference
#Batching
#Latency
Machine Learning Engineer
•
System Design
•
hard
Design a system to continuously evaluate a production LLM for red-teaming vulnerabilities and prompt injection attacks.
#Red Teaming
#Security
#Evaluation Pipelines
Machine Learning Engineer
•
System Design
•
hard
Design a data pipeline to deduplicate, filter, and tokenize a multi-terabyte web scraping dataset for LLM pretraining.
#Data Engineering
#Big Data
#MinHash
#Pretraining
Machine Learning Engineer
•
System Design
•
hard
Design an inference system for Claude that can efficiently handle 100k+ token context windows while serving thousands of concurrent users.
#LLM Serving
#KV Caching
#PagedAttention
#Dynamic Batching
Machine Learning Engineer
•
System Design
•
hard
How would you design the distributed training pipeline for a 100B+ parameter model across 10,000 GPUs?
#Distributed Training
#Megatron-LM
#DeepSpeed
#Network Topology
Difficulty Radar
Based on recent AI-sourced data.
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The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.