← Anthropic Interview Insights
This is the kind of question where the scope alone can paralyze you.
Start by clarifying requirements and constraints (tenants, request classes, SLAs, backend heterogeneity), then present a high-level architecture with key components (router, scheduler, batcher, cache, credit manager) and walk through a request's lifecycle. Finally, dive into the algorithms for prioritization, batching, and cache management, discussing trade-offs and how they meet the requirements.
Pro tip: Emphasize the importance of observability and feedback loops: metrics like queue latency, cache hit rate, and credit utilization should drive dynamic adjustments. Also, mention that the design should be extensible to new backends and policies without major rewrites.
Ask about tenant isolation, request classes (e.g., real-time vs. batch), SLAs, expected traffic patterns, backend capabilities (GPU vs. CPU), and fairness goals. This ensures the design addresses the right problems.
Outline the main components: API gateway, request queue, priority scheduler, dynamic batcher, backend pool (GPU/CPU), result cache, and credit manager. Explain how they interact and the flow of a request.
Describe the end-to-end path: request arrives, is authenticated and classified, enqueued with priority, scheduled based on credits, batched, executed on a backend, cached, and returned. Define key APIs for submission, status, and admin.
Detail the algorithms: weighted fair queuing or deficit round-robin for prioritization; dynamic batching with timeout and size thresholds; cache admission (e.g., LRU-K, TinyLFU) and eviction (e.g., LRU, LFU) policies.
Discuss trade-offs (latency vs. throughput, fairness vs. utilization, cache hit rate vs. memory) and how to monitor and adapt. Mention potential extensions like multi-region, autoscaling, and A/B testing of policies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They pushed on this as a follow-up once the base design was on the board.
Start by clarifying the system's goals and constraints, then systematically address each concern with concrete mechanisms and trade-offs. Emphasize how you would instrument, monitor, and iterate on the design to handle failures and evolving requirements.
Pro tip: Frame your answer around SLOs and error budgets—this shows you prioritize user impact and can make principled trade-offs between reliability and cost/latency.
Ask about expected traffic patterns, model size distribution, tenant count, latency SLOs, and failure tolerance. Establish what 'good' looks like for each concern.
Propose multi-tenant isolation via resource quotas, separate queues, or dedicated instances. Address hot keys with techniques like key salting, local caching, or request coalescing.
Describe mechanisms like bounded queues, token buckets, and adaptive concurrency limits. Explain how to prioritize critical tenants or requests during overload.
Use model-aware routing (e.g., size-based batching, tiered instances) and straggler mitigation (e.g., speculative execution, hedged requests, timeouts with retries).
Detail node loss handling (health checks, failover, replication), retry policies with idempotency, and comprehensive monitoring for SLAs and debugging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the part of the question I felt most comfortable with.
Start by clarifying the system's workload characteristics and SLOs, then walk through a capacity planning process that ties demand forecasting to resource provisioning. Describe scaling strategies (horizontal/vertical, predictive/reactive) and the monitoring signals that trigger them, emphasizing ML-specific metrics like model latency and GPU utilization. Close with a feedback loop for continuous improvement.
Pro tip: Tie every scaling decision to a concrete SLO and cost trade-off—Anthropic values pragmatic engineering that balances reliability with efficiency. Mention how you'd validate capacity plans with load tests and canary deployments before full rollout.
Ask about the system's purpose, expected traffic patterns, and service level objectives (e.g., p99 latency, throughput). This grounds your strategy in concrete requirements.
Explain how you'd estimate future load using historical data, growth projections, and seasonality. Translate demand into resource needs (e.g., GPU hours, memory) with headroom for spikes.
Describe when to scale horizontally vs. vertically, and whether to use reactive (auto-scaling based on metrics) or predictive (scheduled) scaling. Include ML-specific considerations like model versioning and batch vs. real-time inference.
List key signals: system-level (CPU/GPU utilization, memory, network), application-level (latency, error rates, queue depth), and ML-specific (model drift, prediction confidence, feature freshness). Explain how alerts are set with thresholds and escalation.
Emphasize continuous improvement: load testing, canary deployments, and post-mortems to refine capacity models and scaling policies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: I structured it as a two-by-two between batch size and latency budget, then talked about running shadow traffic experiments to measure cache hit lift without affecting prod.
Start by framing the fundamental tension: latency, throughput, and cost are interdependent, and optimizing one often degrades the others. Then propose a systematic experimentation approach—define metrics, set up A/B tests or canary deployments, and iterate on batching and caching parameters to find the optimal trade-off for the given workload.
Pro tip: Emphasize that the optimal trade-off is workload-dependent and must be validated with real traffic; mention that you'd start with a small percentage of traffic to avoid regressions, and use guardrail metrics to catch unintended consequences.
Clarify the business and technical goals: target latency SLOs, throughput requirements, and cost constraints. Define primary and guardrail metrics (e.g., p99 latency, QPS, cost per inference, error rate).
Explain how batching increases throughput and reduces cost per request but can increase latency, while caching reduces latency and cost but may serve stale results. Discuss how these interact with hardware utilization and model size.
Propose A/B tests or multi-armed bandits to tune batching size, timeout, and cache TTL. Use canary deployments to limit risk, and ensure experiments are statistically powered to detect meaningful differences.
Collect data, analyze trade-offs using Pareto frontiers, and select the configuration that best meets objectives. Iterate as workload patterns change, and consider adaptive batching/caching based on load.
Continuously monitor key metrics in production, set up alerts for SLO violations, and use guardrail metrics to automatically roll back changes that degrade user experience or cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.