Anthropic Software Engineer Interview Loop
Anthropic's software engineer interview starts with a CodeSignal assessment. It's one problem that gets harder across four levels, and failing a level blocks the ones after it. A technical assessment follows in three stages, one of them on concurrency. Then there's a system design round, and its prompts center on serving models. The one candidates mention most is batching inference on a single GPU, with up to 100 inputs, while users wait for answers. You can use AI to prepare, but it's banned in the assessment and the live rounds.
What decides it
Getting through all four levels in time. Each level extends the last one, so a clean structure at level one is what makes level four possible.
Practice by round
Easy algorithm problems, for fluency. The assessment is timed, and each level builds on the code you wrote for the one before.
Practical problems in several parts, where the requirements grow as you go. Examples are an in-memory key-value store, a bank account system and an in-memory file system, plus a token-bucket rate limiter and a task scheduler with cooldown. Practice threads, locks and shared state for the concurrency stage too.
System design at an AI company: batching inference on one GPU, checkpointing a long training run, choosing between a faster model and a better one, and guardrails for an agent that takes actions. State the limit first, like latency, memory or cost, then design to it.
| Case study | Difficulty |
|---|---|
| Inference Batching, One GPU | Medium |
| Checkpointing Multi-Day Training | Medium |
| Shadow Deployment & Canary Rollout | Medium |
| Faster vs. Better Model Trade-off | Medium |
| Guardrails for an Autonomous Agent | Hard |
The LLM concepts an engineer at Anthropic works with: how features get evaluated, what drives their cost, and how they're protected against prompt injection.
| Question | Difficulty |
|---|---|
| Multi-Head Attention | Easy |
| Scaled Dot-Product Attention: Why Scale by √d? | Easy |
| LLM Hallucination | Easy |
| Layer Norm vs. Batch Norm in LLMs | Medium |
| Residual Connections in Transformers | Medium |