Anthropic Four-Round VO (Offer, August): Call-Stack Profiler + Streaming LLM Inference API + Lock-Free Priority Queue

Anthropic's four-round process makes a lot of candidates anxious. I hope sharing how I approached each round helps anyone still grinding through it.

Round 1: Coding — call-stack profiler

• Implement a call-stack profiler: compare consecutive stack samples and emit function start / end events from the differences.
• Extra condition n: a stack must appear n times in a row before the state updates.
• You need to maintain counters and a state machine, and handle all kinds of edge cases.

Round 2: System design — streaming LLM inference API

Design a streaming LLM inference API with:

• Multi-tenant isolation
• Rate limiting
• Canary / gradual rollouts

Architecture: the gateway checks permissions and quotas; the scheduling layer does dynamic batching.

Key discussion points:

• Time to first token (TTFT)
• KV cache
• GPU memory protection
• Reconnecting after a dropped stream
• Idempotency to avoid double billing

Round 3: Coding — concurrency-safe priority queue

• Hand-write a lock-free priority queue that supports dynamically changing priorities.
• Tests concurrency knowledge such as CAS and memory barriers.
• Code robustness is the focus; not many extension questions.

Round 4: Behavioral — conflicting values and technical trade-offs

This AI-culture behavioral question shows up in almost every Anthropic loop.

Question: the model has a safety vulnerability, but fixing it increases inference latency by 30%. What do you do?

Follow-ups:

• How do you quantify the risk?
• How do you align with product?
• Do you have project experience where you prioritized safety?