← Anthropic Interview Insights

Anthropic·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
May 2026

Summary

System design round at Anthropic for a software engineering role, focused entirely on building a background processing backend for LLM prompts. The question had a ton of surface area and the follow-up on streaming and cancellation caught me pretty flat-footed.

Questions Asked (2)

Q1

Design a background processing system for LLM prompts where clients submit jobs via an API and retrieve results through polling or callbacks. Cover job queuing, prioritization, worker pools, model routing, prompt versioning, idempotency, retries, dead-letter queues, result storage, and observability. Also address scaling, cost control, rate limiting, PII handling, and SLAs.

System DesignAPI & IntegrationsTechnical Trade-offs
Author's notes

This thing has so many moving parts I didn't know where to start.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then walk through the high-level architecture covering the core components (API, queue, workers, storage, observability). Dive into key design decisions and trade-offs for each component, and finally discuss operational concerns like scaling, cost, PII, and SLAs.

Pro tip: Emphasize idempotency and dead-letter queues early, as they are critical for reliability in distributed systems and often overlooked. Also, tie cost control to model routing and prompt versioning to show business awareness.

1. Clarify Requirements and Constraints

Ask about expected throughput, latency SLAs, model types, data sensitivity, and budget constraints to tailor the design.

2. High-Level Architecture

Outline the main components: API gateway, job queue, worker pool, model router, result storage, and observability stack.

3. Deep Dive into Key Components

Discuss job queuing (e.g., Kafka, SQS), prioritization (multiple queues or priority levels), worker scaling, model routing (based on cost/latency), prompt versioning, idempotency (idempotency keys), retries with backoff, and dead-letter queues.

4. Operational Concerns

Address scaling (horizontal scaling of workers, auto-scaling), cost control (budget alerts, model selection), rate limiting (per client, per model), PII handling (encryption, anonymization), and SLAs (monitoring, alerting).

5. Trade-offs and Alternatives

Highlight trade-offs such as polling vs callbacks, at-least-once vs exactly-once delivery, and synchronous vs asynchronous processing.

Key Points to Mention

  • Idempotency: Use idempotency keys to ensure duplicate job submissions don't cause duplicate processing.
  • Dead-letter queues: Capture failed jobs after retries for manual inspection and reprocessing.
  • Model routing: Route prompts to appropriate models based on cost, latency, and capability, with fallback strategies.
  • Prompt versioning: Store prompt templates with versions to enable reproducibility and A/B testing.
  • Observability: Implement logging, metrics, and tracing for job lifecycle, queue depths, and error rates.
  • Cost control: Monitor token usage, set budgets, and use cheaper models for non-critical tasks.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

How would you extend the system to support streaming partial outputs from the model back to the client, and allow clients to cancel in-flight jobs?

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

Blanked for a second here.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what does 'partial outputs' mean (token-by-token vs. chunked), expected latency, and client types. Then propose a streaming protocol (e.g., SSE, WebSocket, or HTTP chunked) and a cancellation mechanism (e.g., client disconnect, explicit cancel endpoint, or job ID with abort signal). Finally, discuss trade-offs around state management, backpressure, and resource cleanup.

Pro tip: Emphasize idempotency and graceful degradation: ensure that cancellation doesn't leave orphaned resources and that partial outputs are delivered reliably even if the client reconnects. Mention that you'd instrument metrics for stream duration and cancellation rates to monitor system health.

1. Clarify requirements and constraints

Ask about expected output granularity (token vs. sentence), client capabilities (browser, mobile), and latency/throughput goals. This shapes protocol and infrastructure choices.

2. Choose a streaming transport

Evaluate options like Server-Sent Events (SSE), WebSockets, or HTTP/2 streaming based on bidirectional needs, firewall compatibility, and scalability. For unidirectional server-to-client, SSE is often simplest.

3. Design the streaming API and data format

Define a clear message schema (e.g., JSON lines with event types: 'partial', 'complete', 'error'). Include a job ID for correlation and support resumability if needed.

4. Implement cancellation semantics

Provide a cancel endpoint (e.g., DELETE /jobs/{id}) or use connection close as a signal. Ensure the server aborts model inference, releases resources, and notifies downstream systems.

5. Address operational concerns

Discuss backpressure handling, timeouts, retries, and monitoring. Consider how to scale streaming connections (e.g., load balancers, sticky sessions) and handle failures gracefully.

Key Points to Mention

  • Use of Server-Sent Events (SSE) or WebSockets for streaming, with trade-offs (e.g., SSE is unidirectional but simpler; WebSockets allow bidirectional control).
  • Job ID and correlation: assign a unique ID to each inference request to track and cancel jobs.
  • Cancellation propagation: how to signal the model server to stop generation (e.g., via gRPC cancellation or a message queue).
  • Resource cleanup: ensuring GPU memory, threads, and database connections are released upon cancellation.
  • Backpressure and flow control: handling slow clients without blocking the server (e.g., buffering limits, dropping partials).
  • Idempotency and retries: making cancellation idempotent and handling client reconnects with resumable streams.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.