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

SeniorPrefer not to say

Jun 2026Remote

Summary

Anthropic ML infrastructure interview, system design round focused entirely on building a production inference serving platform. Pretty deep dive, they wanted real tradeoffs not just a laundry list of components.

Questions Asked (1)

Design an ML inference API service that can host multiple trained models and serve predictions over HTTP or gRPC. Walk through request routing, batching strategy, autoscaling, GPU and CPU resource allocation, model versioning with canary and shadow rollouts, monitoring for latency and drift, and how you'd define and meet reliability SLOs.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This was basically the entire interview in one question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (traffic patterns, model types, latency SLOs, hardware budget), then design the system in layers: API gateway, routing, batching, resource management, deployment, and observability. Emphasize trade-offs and how each component contributes to meeting reliability SLOs, and discuss failure modes and mitigations.

Pro tip: Anchor your design around SLOs and error budgets from the start—this shows you prioritize reliability and can justify architectural decisions with quantitative reasoning. Also, mention that you'd start with a simple solution and evolve it as scale demands, demonstrating pragmatism.

1. Clarify Requirements and Constraints

Ask about expected QPS, latency targets, model types (size, framework), hardware availability (GPU/CPU), and deployment environment (cloud, on-prem). This ensures your design is grounded in reality.

2. High-Level Architecture

Outline the main components: API gateway (HTTP/gRPC), request router, model registry, inference workers, and monitoring. Explain how requests flow from client to prediction.

3. Deep Dive into Key Components

Detail routing (model/version selection), batching strategies (dynamic batching, max batch size, timeout), autoscaling (metrics like queue depth, GPU utilization), and resource allocation (GPU sharing, CPU pinning).

4. Deployment and Versioning

Describe model versioning, canary rollouts (traffic splitting), shadow deployments (mirroring), and rollback strategies. Discuss how to manage model artifacts and metadata.

5. Monitoring, SLOs, and Reliability

Define SLOs (latency, availability, error rate), monitoring for drift (data/prediction), and alerting. Explain how to use error budgets and incident response to maintain reliability.

Key Points to Mention

Dynamic batching with timeout and max batch size to balance latency and throughput
Autoscaling based on custom metrics (queue length, GPU utilization) and predictive scaling
GPU memory management and multi-model serving (e.g., NVIDIA Triton, MPS)
Canary and shadow deployments for safe model rollout, with automated rollback
Monitoring for data drift and concept drift, with retraining triggers
SLOs with error budgets, and how to meet them via redundancy, circuit breakers, and graceful degradation

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