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

SeniorRejected
May 2026

Summary

Went into a system design round at Anthropic feeling pretty solid, but the interviewer had other plans. The question was around batch GPU inference and instead of letting me drive, they kept drilling into specifics I hadn't prepared for. Walked out knowing I'd bombed it.

Questions Asked (1)

Q1

Design a batch GPU inference API. How would you architect it end-to-end, including queue design, scaling, and resource management?

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

I had a prepared flow and it fell apart fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (batch size, latency, throughput, model size, hardware) and then walk through the end-to-end architecture: API layer, queue, batching, GPU workers, and autoscaling. Emphasize trade-offs between latency and throughput, and how you handle failures, backpressure, and resource isolation.

Pro tip: Show awareness of GPU memory fragmentation and the cost of model loading; propose techniques like dynamic batching, model caching, and multi-model serving to maximize utilization. Also mention observability and cost tracking as first-class concerns.

1. Clarify Requirements and Constraints

Ask about expected request rate, batch sizes, latency SLOs, model types (e.g., LLMs), and hardware (GPU types, memory). This shapes the entire design.

2. Design the API and Queueing Layer

Define a REST/gRPC API for submitting batch jobs and checking status. Use a distributed queue (e.g., Redis, SQS, Kafka) to decouple submission from processing, with priorities and dead-letter queues.

3. Architect the Inference Workers

Workers pull batches from the queue, load models onto GPUs, and run inference. Implement dynamic batching to group requests, and manage GPU memory with model caching and eviction policies.

4. Implement Scaling and Resource Management

Autoscale workers based on queue depth and GPU utilization. Use Kubernetes with GPU node pools, and consider multi-model serving and model parallelism for large models.

5. Address Reliability, Observability, and Cost

Add retries, idempotency, and circuit breakers. Monitor latency, throughput, GPU memory, and cost per inference. Log and trace requests for debugging.

Key Points to Mention

  • Dynamic batching to improve GPU utilization and throughput
  • Queue design with priorities, backpressure, and dead-letter queues
  • Autoscaling based on queue depth and GPU metrics
  • GPU memory management: model caching, eviction, and fragmentation
  • Multi-model serving and model parallelism for large models
  • Observability: metrics, logging, tracing, and cost tracking

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