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
•
Behavioral
•
medium
Tell me about a time you had to make a trade-off between model performance (e.g., accuracy or helpfulness) and model safety or fairness. How did you approach the decision?
#Safety
#Ethics
#Trade-offs
#Decision Making
Machine Learning Engineer
•
Behavioral
•
easy
Anthropic places a heavy emphasis on AI safety. Why do you want to work in AI alignment, and what do you think is the biggest unsolved problem in the field today?
#AI Safety
#Motivation
#Alignment
#Industry Trends
Machine Learning Engineer
•
Behavioral
•
medium
Tell me about a time you had to trade off model performance (e.g., accuracy or helpfulness) for safety, fairness, or alignment.
#AI Safety
#Ethics
#Decision Making
Machine Learning Engineer
•
Behavioral
•
medium
Anthropic places a high value on AI safety. Describe a time you identified a potential negative impact or safety flaw in your work and how you addressed it.
#AI Safety
#Ethics
#Proactivity
Machine Learning Engineer
•
Behavioral
•
medium
Tell me about a time you strongly disagreed with a fellow researcher or engineer on the direction of a model architecture or training pipeline.
#Conflict Resolution
#Collaboration
#Communication
Machine Learning Engineer
•
Behavioral
•
medium
Describe a situation where you had to debug a silent failure (e.g., loss not converging, degraded outputs) in a complex machine learning pipeline.
#Debugging
#Machine Learning
#Problem Solving
Machine Learning Engineer
•
Behavioral
•
medium
How do you prioritize research ideas when working on an open-ended problem like hallucination reduction in LLMs?
#Research Strategy
#Prioritization
#Innovation
Machine Learning Engineer
•
Behavioral
•
medium
Tell me about a time you had to optimize a piece of code that was bottlenecking a critical ML pipeline or training run.
#Performance Optimization
#Profiling
#Engineering
Machine Learning Engineer
•
Behavioral
•
medium
Tell me about a time you had to delay a model release or feature because of a safety, bias, or alignment concern.
#AI Safety
#Ethics
#Decision Making
Machine Learning Engineer
•
Behavioral
•
medium
Anthropic highly values 'helpful, honest, and harmless' (HHH) models. Describe a situation where these three traits conflicted in a project you worked on.
#HHH
#Alignment
#Trade-offs
Machine Learning Engineer
•
Behavioral
•
medium
Tell me about a time a research experiment or model training run failed completely. How did you pivot and what did you learn?
#Resilience
#Debugging
#Research
Machine Learning Engineer
•
Behavioral
•
medium
How do you balance the pressure to ship capabilities quickly with the need for rigorous safety testing and alignment?
#Prioritization
#Safety vs Capabilities
#Communication
Machine Learning Engineer
•
Behavioral
•
medium
Describe a time when you strongly disagreed with a senior researcher or engineer about the technical direction of an ML project. How was it resolved?
#Conflict Resolution
#Collaboration
#Ego
Machine Learning Engineer
•
Coding
•
hard
Implement a multi-head self-attention mechanism from scratch in PyTorch. Ensure your implementation efficiently handles batched inputs and causal masking.
#PyTorch
#Transformers
#Attention Mechanism
#Vectorization
Machine Learning Engineer
•
Coding
•
medium
Write a Python function to efficiently perform top-k and nucleus (top-p) sampling given a 1D tensor of logits.
#Sampling
#Inference
#Probability
#PyTorch
Machine Learning Engineer
•
Coding
•
medium
Implement a distributed all-reduce operation using a ring topology. You can write pseudo-code assuming basic send() and recv() primitives.
#Networking
#All-reduce
#Algorithms
#Parallel Computing
Machine Learning Engineer
•
Coding
•
easy
Given a string representing a mathematical expression, write a tokenizer that converts it into a list of valid tokens (numbers, operators, parentheses). Handle multi-digit numbers and ignore whitespace.
#Tokenization
#Parsing
#Strings
#State Machines
Machine Learning Engineer
•
Coding
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medium
Implement a basic tokenizer using Byte-Pair Encoding (BPE) given a corpus of text and a target vocabulary size.
#NLP
#Tokenization
#String Processing
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
Write a PyTorch script to implement simple data parallelism using DistributedDataParallel (DDP), including the setup of the process group.
#PyTorch
#DDP
#Multiprocessing
Machine Learning Engineer
•
Coding
•
medium
Implement a Trie data structure to efficiently filter out a large list of toxic words from a continuous stream of generated tokens.
#Data Structures
#Trie
#String Manipulation
Machine Learning Engineer
•
Coding
•
medium
Write a Python function to sample from a logits distribution using top-k and top-p (nucleus) sampling.
#Sampling
#Probability
#PyTorch
Machine Learning Engineer
•
Coding
•
hard
Given a sequence of characters and a vocabulary of merges, implement the Byte-Pair Encoding (BPE) tokenization merging algorithm.
#Tokenization
#NLP
#Greedy Algorithms
Machine Learning Engineer
•
Coding
•
easy
Implement a sliding window attention mask generator for a sequence of length N and window size W.
#Matrix Operations
#Attention
#PyTorch
Machine Learning Engineer
•
Coding
•
medium
Write an algorithm to find the longest common substring between two large text documents efficiently.
#Dynamic Programming
#Strings
#Suffix Trees
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
•
Coding
•
medium
Implement a multi-head self-attention mechanism from scratch in PyTorch, ensuring it is highly optimized for batch processing.
#PyTorch
#Transformers
#Linear Algebra
Machine Learning Engineer
•
Coding
•
hard
Write a function to perform Rotary Positional Embeddings (RoPE) on a given query and key tensor.
#PyTorch
#Transformers
#Positional Encodings
Machine Learning Engineer
•
Coding
•
medium
Given a massive log file of model training loss, write a script to detect loss spikes and automatically identify the corrupted data batch.
#Python
#Log Parsing
#Anomaly Detection
Machine Learning Engineer
•
Coding
•
hard
Implement a custom PyTorch autograd function for a novel activation function, including both the forward and backward passes.
#PyTorch Internals
#Calculus
#Autograd
Machine Learning Engineer
•
Coding
•
medium
Write an algorithm to efficiently sample from a logits distribution using Top-K and Top-P (Nucleus) sampling.
#Probability
#Sampling
#Sorting
Machine Learning Engineer
•
Coding
•
hard
Implement a memory-efficient Ring Attention mechanism to handle extremely long context windows across multiple GPUs.
#Distributed Computing
#Attention
#Memory Optimization
Machine Learning Engineer
•
Coding
•
medium
Given a stream of generated tokens, write a highly optimized Trie-based data structure to filter out a dynamic list of toxic phrases in real-time.
#Data Structures
#Trie
#Streaming
Machine Learning Engineer
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Coding
•
medium
Write a Python script using multiprocessing to efficiently tokenize and shard a massive JSONL dataset into binary memmap files.
#Multiprocessing
#I/O
#Tokenization
Machine Learning Engineer
•
Coding
•
hard
Implement the forward pass of a Mixture of Experts (MoE) layer with a top-2 routing mechanism.
#MoE
#PyTorch
#Routing
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 data pipeline to process and filter petabytes of web-scraped text for pre-training a foundational LLM. How do you handle exact and fuzzy deduplication at this scale?
#Data Pipeline
#Deduplication
#MinHash
#Big Data
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
Machine Learning Engineer
•
System Design
•
hard
Design a data deduplication pipeline for a 5-trillion token pretraining dataset.
#Big Data
#MinHash
#LSH
#Distributed Processing
Machine Learning Engineer
•
System Design
•
medium
Design a red-teaming platform that automatically generates adversarial prompts to test Claude's safety boundaries.
#Red Teaming
#Adversarial ML
#Evaluation
Machine Learning Engineer
•
System Design
•
medium
Design a continuous evaluation system that benchmarks daily model checkpoints against a suite of 50+ reasoning, coding, and safety tasks.
#Evaluation
#CI/CD for ML
#Orchestration
Machine Learning Engineer
•
System Design
•
hard
Design a fault-tolerant checkpointing system for a massive training run that minimizes GPU idle time during saves.
#Checkpointing
#I/O Optimization
#Fault Tolerance
Machine Learning Engineer
•
System Design
•
hard
How would you architect an API rate-limiting and dynamic batching system for Claude to maximize GPU utilization while guaranteeing latency SLAs?
#API Design
#Dynamic Batching
#Concurrency
Machine Learning Engineer
•
Technical
•
hard
Explain the mathematical formulation of RLHF (Reinforcement Learning from Human Feedback). Specifically, how does the PPO objective function work, and what are the common failure modes when fine-tuning a large language model?
#RLHF
#PPO
#Model Alignment
#Optimization
Machine Learning Engineer
•
Technical
•
medium
Describe Anthropic's Constitutional AI. How does it differ from standard RLHF, and how would you implement the critique and revision pipeline programmatically?
#Constitutional AI
#RLAIF
#Prompt Engineering
#Alignment
Machine Learning Engineer
•
Technical
•
medium
How does Rotary Positional Embedding (RoPE) work compared to absolute positional embeddings, and why is it preferred in modern LLMs?
#Embeddings
#Transformers
#RoPE
#Linear Algebra
Machine Learning Engineer
•
Technical
•
hard
Explain the concept of 'Scaling Laws' in language models (e.g., Chinchilla scaling laws). If you have a fixed compute budget, how do you determine the optimal model size and number of training tokens?
#Scaling Laws
#Compute Optimal
#Pre-training
#Resource Allocation
Machine Learning Engineer
•
Technical
•
medium
What is FlashAttention? Explain how it optimizes memory bandwidth and reduces the time complexity of the attention mechanism.
#FlashAttention
#Memory Bandwidth
#CUDA
#Hardware Optimization
Machine Learning Engineer
•
Technical
•
hard
Discuss the trade-offs between Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ) for deploying a large language model. How do techniques like AWQ or GPTQ mitigate performance degradation?
#Quantization
#Model Compression
#Inference
#AWQ/GPTQ
Machine Learning Engineer
•
Technical
•
medium
Explain the differences between Rotary Positional Embeddings (RoPE), ALiBi, and absolute positional embeddings. Why are relative positional embeddings preferred in modern LLMs?
#Transformers
#Positional Encoding
#LLM Architecture
Machine Learning Engineer
•
Technical
•
hard
How does FlashAttention work at a hardware level, and why does it reduce the memory complexity of the attention mechanism from O(N^2) to O(N)?
#Hardware Optimization
#CUDA
#Memory Hierarchy
#FlashAttention
Machine Learning Engineer
•
Technical
•
hard
Derive the memory requirements for training a 70B parameter model in mixed precision using AdamW and ZeRO-3 optimization.
#Distributed Training
#DeepSpeed
#Memory Profiling
Machine Learning Engineer
•
Technical
•
medium
Explain the concept of the KV cache in autoregressive decoding. How does PagedAttention optimize this process?
#LLM Inference
#Memory Management
#PagedAttention
Machine Learning Engineer
•
Technical
•
medium
How does Constitutional AI differ from standard Reinforcement Learning from Human Feedback (RLHF)?
#Constitutional AI
#RLHF
#Alignment
Machine Learning Engineer
•
Technical
•
hard
Explain the Proximal Policy Optimization (PPO) algorithm used in RLHF. What are its common failure modes in language model fine-tuning?
#PPO
#RLHF
#Optimization
Machine Learning Engineer
•
Technical
•
hard
What is Direct Preference Optimization (DPO) and how does it compare mathematically and practically to PPO?
#DPO
#RLHF
#Loss Functions
Machine Learning Engineer
•
Technical
•
medium
Explain the difference between Tensor Parallelism (e.g., Megatron-LM) and Pipeline Parallelism. When would you use each?
#Tensor Parallelism
#Pipeline Parallelism
#Model Scaling
Machine Learning Engineer
•
Technical
•
medium
How do you handle straggler nodes or hardware failures in synchronous distributed training of large language models?
#Fault Tolerance
#Distributed Training
#Infrastructure
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
Machine Learning Engineer
•
Technical
•
medium
How does weight decay interact with the Adam optimizer compared to standard SGD? Why was AdamW introduced?
#Optimizers
#AdamW
#Regularization
Machine Learning Engineer
•
Technical
•
hard
What is the Gumbel-Softmax trick, and in what scenarios would you use it in language modeling or reinforcement learning?
#Generative Models
#Reparameterization
#Math
Machine Learning Engineer
•
Technical
•
medium
Explain how quantization (e.g., INT8, AWQ, GPTQ) affects model weights and activations. What are the trade-offs in perplexity vs inference speed?
#Quantization
#Inference
#Model Compression
Machine Learning Engineer
•
Technical
•
hard
How would you implement speculative decoding to speed up autoregressive inference? What are the requirements for the draft model?
#Speculative Decoding
#Latency Optimization
#Algorithms
Machine Learning Engineer
•
Technical
•
medium
Explain Constitutional AI and how its pipeline differs from standard Reinforcement Learning from Human Feedback (RLHF).
#Constitutional AI
#RLHF
#AI Safety
Machine Learning Engineer
•
Technical
•
hard
How does Direct Preference Optimization (DPO) mathematically eliminate the need for an explicit reward model compared to PPO?
#RLHF
#DPO
#Optimization
Machine Learning Engineer
•
Technical
•
medium
Explain the KV cache in transformer inference. How do techniques like PagedAttention or Ring Attention optimize it?
#Inference Optimization
#Memory Management
#Attention Mechanisms
Machine Learning Engineer
•
Technical
•
hard
What are the specific trade-offs between Tensor Parallelism, Pipeline Parallelism, and Fully Sharded Data Parallel (FSDP)?
#Distributed Training
#Parallelism
#GPU Memory
Machine Learning Engineer
•
Technical
•
medium
How do scaling laws apply to model parameters vs. dataset size? Explain the Chinchilla optimal ratio.
#Scaling Laws
#Compute Optimal Training
Machine Learning Engineer
•
Technical
•
hard
Describe mechanistic interpretability. How would you isolate the specific attention head responsible for a specific bias in a Large Language Model?
#Mechanistic Interpretability
#Activation Patching
#Probing
Machine Learning Engineer
•
Technical
•
medium
What is the impact of mixed-precision training (e.g., BF16 vs FP16) on model convergence and memory? Why is BF16 generally preferred for LLMs?
#Numerical Precision
#Hardware
#Training Stability
Machine Learning Engineer
•
Technical
•
hard
Explain the concept of 'sycophancy' in LLMs. How would you design a training objective or dataset to reduce it?
#Sycophancy
#RLHF
#Data Generation
Machine Learning Engineer
•
Technical
•
medium
How does Grouped-Query Attention (GQA) bridge the gap between Multi-Head Attention (MHA) and Multi-Query Attention (MQA)?
#Attention Mechanisms
#Inference Efficiency
Machine Learning Engineer
•
Technical
•
hard
What causes 'mode collapse' or 'reward hacking' in RLHF, and what regularization techniques prevent the policy model from drifting too far from the reference model?
#Reinforcement Learning
#KL Divergence
#Reward Hacking
Machine Learning Engineer
•
Technical
•
medium
Explain the differences between LoRA, QLoRA, and full fine-tuning. When would you use each at Anthropic?
#PEFT
#LoRA
#Quantization
Machine Learning Engineer
•
Technical
•
hard
Discuss the phenomenon of 'grokking' in neural networks. How does weight decay influence it, and what are the implications for LLM training?
#Grokking
#Generalization
#Regularization
Machine Learning Engineer
•
Technical
•
medium
What are the mathematical and practical advantages of using SwiGLU over standard ReLU in Transformer feed-forward networks?
#Activation Functions
#Transformers
#Math
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