How to prepare for the Anthropic interview
This track prepares you for Anthropic's Machine Learning Engineer loop, which weighs clean modular Python, concurrency, and from-scratch ML coding alongside heavy ML-systems design (inference/serving, distributed training, eval and safety pipelines) and ML/RL theory with a project deep-dive. Content is prioritized by real interview frequency: high-frequency items are core, mediums recommended, lows stretch.
The Anthropic interview process
- 1. Recruiter Screen & Hiring-Manager Technical Screen~30 min + 45-60 min
A non-trivial recruiter call (motivation, background, 'why Anthropic', mission alignment) and a hiring-manager technical deep-dive into your past ML projects and decisions. The recruiter often names the coding prompt family in advance.
- 2. CodeSignal OA + Live Coding / ML-Ops90 min OA (4 levels) + 1-2 x 55-60 min
A four-level CodeSignal assessment (one 'code that evolves' problem, ~600-pt scale) plus live rounds: practical build-from-scratch and concurrency in Python, an MLE Python/ML-ops task (data cleaning or improving code from an internal tool/agent loop), and ML-coding-from-scratch (attention, BPE, sampling) and debugging. Clean, modular, runnable code matters more than algorithmic tricks. AI tools banned; internet allowed.
- 3. ML / System Design55-60 min (shared Google Doc)
The heart of the MLE loop: inference/serving APIs (batching, KV cache, GPU memory, streaming), distributed-training systems (data/tensor/pipeline parallelism, FSDP, fault tolerance), eval pipelines, data pipelines, retrieval/search at scale, and safety-classifier pipelines.
- 4. ML Theory / RL Fundamentals & Project Deep-Dive55-60 min
ML/RL theory (scaling laws, transformer internals, RL fundamentals incl. on/off-policy and advantage estimation) and a technical project deep-dive on an ML system you built end-to-end (decisions, tradeoffs, failures). Lighter on novel-research presentation than the RS track.
Anthropic interview: frequently asked questions
What is the Anthropic interview process?+
The Anthropic interview typically runs through these stages: Recruiter Screen & Hiring-Manager Technical Screen (~30 min + 45-60 min), CodeSignal OA + Live Coding / ML-Ops (90 min OA (4 levels) + 1-2 x 55-60 min), ML / System Design (55-60 min (shared Google Doc)), ML Theory / RL Fundamentals & Project Deep-Dive (55-60 min). Each stage screens for different skills, from a recruiter screen through technical and system-design rounds.
What happens in the Anthropic Recruiter Screen & Hiring-Manager Technical Screen round?+
A non-trivial recruiter call (motivation, background, 'why Anthropic', mission alignment) and a hiring-manager technical deep-dive into your past ML projects and decisions. The recruiter often names the coding prompt family in advance. Tips: Have a crisp, specific 'why Anthropic' that wouldn't also fit OpenAI; Be…
What happens in the Anthropic CodeSignal OA + Live Coding / ML-Ops round?+
A four-level CodeSignal assessment (one 'code that evolves' problem, ~600-pt scale) plus live rounds: practical build-from-scratch and concurrency in Python, an MLE Python/ML-ops task (data cleaning or improving code from an internal tool/agent loop), and ML-coding-from-scratch (attention, BPE, sampling) and…
What happens in the Anthropic ML / System Design round?+
The heart of the MLE loop: inference/serving APIs (batching, KV cache, GPU memory, streaming), distributed-training systems (data/tensor/pipeline parallelism, FSDP, fault tolerance), eval pipelines, data pipelines, retrieval/search at scale, and safety-classifier pipelines. Tips: Be able to whiteboard an LLM serving…
What happens in the Anthropic ML Theory / RL Fundamentals & Project Deep-Dive round?+
ML/RL theory (scaling laws, transformer internals, RL fundamentals incl. on/off-policy and advantage estimation) and a technical project deep-dive on an ML system you built end-to-end (decisions, tradeoffs, failures). Lighter on novel-research presentation than the RS track. Tips: Prepare 2-3 end-to-end ML systems you…
What projects should I build to prepare for a Anthropic interview?+
Build the resume projects that Anthropic screens for: each one is chosen to signal a specific skill the role tests. Deep-ML's Anthropic track recommends 3 projects and walks you through them.
How long does it take to prepare for a Anthropic interview?+
It depends on your starting point and the role. Deep-ML builds a paced, company-specific plan (2 weeks to 3 months) from a Anthropic interview path, the right resume projects, and timed mock interviews, then tracks your readiness so you know when you're ready.