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 whole thing was a deep critique and redesign of an ML inference API doc, covering pretty much every distributed systems concern you can think of. Dense, but interesting if that's your thing.

Questions Asked (1)

You're reviewing another engineer's design doc for an ML inference API. Critique it and propose improvements covering distributed systems concerns: SLOs, throughput capacity, autoscaling, batching, GPU scheduling, model versioning, multi-tenant isolation, rate limiting, backpressure, retries, cold starts, caching, token streaming, traffic shaping, monitoring, safety filters, and cost controls.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This was basically the entire interview compressed into one prompt.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging the breadth of the review and propose a structured framework that groups concerns into layers (e.g., reliability, scalability, efficiency, safety). Then, for each layer, identify potential gaps in the design doc and suggest concrete improvements, prioritizing based on impact and feasibility. Conclude by emphasizing trade-offs and the need for iterative refinement.

Pro tip: Demonstrate empathy for the design author by framing critiques as questions or suggestions rather than outright flaws, and tie every improvement to a measurable SLO or business outcome to show you think like an owner.

1. Clarify goals and constraints

Ask about the intended use cases, expected traffic patterns, latency and throughput SLOs, and any regulatory or cost constraints. This ensures your critique is grounded in the actual requirements.

2. Assess reliability and scalability

Evaluate how the design handles failures, traffic spikes, and growth. Cover autoscaling, batching, GPU scheduling, cold starts, retries, backpressure, and rate limiting, suggesting improvements like adaptive batching or multi-tier autoscaling.

3. Evaluate efficiency and cost

Analyze caching strategies, token streaming, traffic shaping, and cost controls. Propose optimizations such as KV caching, request coalescing, or spot instance usage with fallback to on-demand.

4. Examine safety and multi-tenancy

Review model versioning, multi-tenant isolation, safety filters, and monitoring. Suggest canary deployments, per-tenant quotas, and real-time safety checks with audit trails.

5. Prioritize and summarize

Group suggestions by impact and effort, and recommend a phased implementation. Highlight any critical gaps that must be addressed before launch.

Key Points to Mention

Define clear SLOs for latency, throughput, and availability, and ensure the design includes mechanisms to monitor and alert on them.
Implement adaptive batching and GPU scheduling to maximize utilization while meeting latency SLOs, considering techniques like continuous batching and MIG.
Design for multi-tenant isolation with per-tenant rate limiting, quotas, and resource isolation to prevent noisy neighbor issues.
Incorporate backpressure and retry strategies with exponential backoff and jitter, and consider circuit breakers to avoid cascading failures.
Use model versioning with canary deployments and rollback capabilities, and ensure safety filters are applied consistently across versions.
Optimize cost through caching (e.g., KV cache, response cache), traffic shaping, and autoscaling policies that balance performance and cost.

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