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

SeniorPrefer not to say

Jun 2026

Summary

System design round at Anthropic for a software engineering role. The question was massive, basically a full ML platform design crammed into one session. Walked away feeling like I touched everything but went deep on nothing.

Questions Asked (1)

Design a low-latency ML inference API for real-time predictions. Cover SLOs, request/response schema, auth, rate limiting, and multitenancy. Then walk through the architecture: load balancing, stateless API tier, feature retrieval, model serving on CPU and GPU, batching, quantization, caching, and autoscaling. Also address model versioning, canary deployments and rollbacks, online A/B testing, observability (metrics, tracing, drift detection, data quality), cost controls, fallback behavior during partial outages, security, PII handling, regionalization, and disaster recovery.

System DesignTechnical Trade-offsA/B Testing & Experimentation

Author's notes

This question is basically a whole system design interview loop compressed into one prompt.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining SLOs, then present a high-level architecture before diving into each component. Structure your answer to cover all requested areas logically, emphasizing trade-offs and how you would validate the design.

Pro tip: Focus on the critical path for latency (feature retrieval, model inference) and propose concrete techniques like caching, batching, and quantization with quantified benefits. Show awareness of operational challenges like canary deployments and drift detection, and tie them to business impact.

1. Clarify Requirements and Define SLOs

Ask questions to understand expected QPS, latency targets (e.g., p99 < 100ms), model size, and update frequency. Define SLOs for availability, latency, and accuracy.

2. Design API Contract and Security

Specify request/response schema (e.g., JSON with input features and model version), authentication (API keys, OAuth), rate limiting (token bucket per tenant), and multitenancy isolation.

3. Outline High-Level Architecture

Describe load balancing, stateless API tier, feature store retrieval, model serving on CPU/GPU, and caching layers. Explain how components interact to meet SLOs.

4. Detail Optimization and Scaling Strategies

Discuss batching (dynamic/static), quantization (FP16, INT8), caching (feature and prediction), and autoscaling policies based on latency and queue depth.

5. Address Operational Excellence

Cover model versioning, canary deployments, rollbacks, A/B testing, observability (metrics, tracing, drift detection), cost controls, fallback behavior, security, PII handling, regionalization, and disaster recovery.

Key Points to Mention

SLOs: define latency, availability, and accuracy targets; monitor and alert on them.
Stateless API tier with horizontal scaling and load balancing for high availability.
Feature retrieval: use a low-latency feature store with caching (e.g., Redis) and precomputed features.
Model serving: separate CPU and GPU pools; use batching and quantization to optimize latency and cost.
Canary deployments and A/B testing: route a small percentage of traffic to new models, monitor metrics, and rollback if issues.
Observability: implement distributed tracing, metrics (latency, error rates), and drift detection; handle PII with encryption and anonymization.

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