← Anthropic Interview Insights
I went broad and flagged a bunch of issues across the doc, which felt thorough at the time.
Start by clarifying the doc's goals and constraints (e.g., latency, throughput, safety, cost) to ground your critique. Then systematically evaluate the design across key dimensions like API contract, scalability, reliability, and security, proposing concrete fixes with trade-offs. Conclude by prioritizing the most impactful issues and suggesting validation steps.
Pro tip: Frame your critique as collaborative and improvement-oriented, not just fault-finding. Acknowledge what the doc does well before diving into problems, and always tie fixes back to user or business impact.
Ask about the intended use cases, expected traffic, latency SLOs, safety requirements, and cost constraints. This ensures your critique is relevant and not based on assumptions.
Check if the API is intuitive, versioned, and consistent. Look for issues like lack of pagination, unclear error codes, or missing idempotency keys for retries.
Review how the design handles high load: batching, caching, rate limiting, and autoscaling. Identify bottlenecks like synchronous calls to slow services or lack of backpressure.
Look for single points of failure, retry strategies, circuit breakers, and graceful degradation. Ensure the design includes monitoring, logging, and alerting.
Suggest concrete improvements, explaining the trade-offs (e.g., added complexity vs. reliability). Prioritize based on impact and effort, and recommend validation via load testing or canary deployments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was clearly the crux of the whole interview and we barely touched it.
Start by clarifying the requirements and constraints of the inference API, such as latency SLAs, throughput targets, and cost considerations. Then, outline a batch processing strategy that balances efficiency and responsiveness, covering dynamic batching, queue management, and resource allocation. Finally, discuss trade-offs and how you would measure and iterate on the strategy.
Pro tip: Emphasize the importance of adaptive batching based on real-time load and latency budgets, and mention how you would handle tail latency to maintain user experience. Show awareness of cost implications and the need for monitoring to continuously optimize batch sizes.
Ask about expected request patterns, latency SLAs, throughput goals, and cost constraints to ground your strategy in concrete numbers.
Propose a dynamic batching system that groups incoming requests based on time windows or size thresholds, with configurable parameters.
Describe how requests are queued, prioritized, and dispatched to inference workers, including handling of timeouts and retries.
Discuss trade-offs between latency and throughput, and how to handle spikes, failures, and varying request sizes.
Explain how you would instrument the system, track key metrics (e.g., p99 latency, batch size distribution), and adjust parameters based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.