← Anthropic Interview Insights
This is basically the whole interview, not a warmup.
Start by clarifying requirements: expected QPS, latency SLOs, batch size limits, and failure semantics. Then design the system in layers: a synchronous API gateway that enqueues requests, a batching scheduler that groups requests by model and parameters, and a dispatcher that sends batches to GPU servers. Finally, discuss trade-offs like latency vs. throughput, timeout handling, and backpressure.
Pro tip: Emphasize that batching must respect per-request latency SLOs; use a dynamic batching window that adapts to load, and always include a timeout to prevent starvation. Also, mention that you'd start with a simple fixed-window batching and iterate based on metrics.
Ask about expected request rate, latency SLOs, model types, GPU memory limits, and whether requests can be delayed. This sets the stage for design decisions.
Define a synchronous HTTP endpoint that accepts a prediction request, returns a response, and internally enqueues the request. Describe how the client waits (e.g., long polling, futures) and how timeouts are handled.
Explain how requests are aggregated: a batching queue that collects requests until a batch size or time window is reached. Discuss dynamic batching, priority, and grouping by model/parameters.
Describe how batches are sent to GPU servers (e.g., via gRPC), how results are demultiplexed back to individual requests, and how to handle failures and retries.
Discuss latency vs. throughput, backpressure, monitoring, and scaling. Mention how to tune batch size and timeout based on load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I jumped straight to a routing table with version metadata and per-tenant rate limiting at the API gateway layer.
Start by clarifying requirements: what models/versions exist, how tenants are identified, and what quota dimensions matter (requests, tokens, cost). Then propose a layered architecture: a routing layer that selects models based on policy, and a quota enforcement layer that tracks usage per tenant with atomic counters and fallback strategies.
Pro tip: Emphasize idempotency and graceful degradation: quotas should be enforced without dropping requests mid-flight, and routing should have a fallback model if the primary is unavailable or over quota. Also mention observability—metrics and logs are crucial for debugging quota issues and routing decisions.
Ask about the number of models/versions, tenant scale, quota dimensions (requests, tokens, cost), and latency/consistency requirements. This shapes the design.
Propose a routing service that uses tenant policies, model availability, and versioning rules to select the appropriate model. Consider canary releases, A/B testing, and fallback models.
Use a distributed counter (e.g., Redis) with atomic increments to track per-tenant usage. Enforce quotas at the edge or gateway, and decide between hard/soft limits and burst allowances.
Address race conditions, quota resets, and model unavailability. Implement retries, circuit breakers, and fallback to cheaper models or queueing when quotas are exceeded.
Add logging, metrics, and tracing for routing decisions and quota usage. Use this data to tune quotas and routing policies over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Request coalescing for duplicate inputs is a fun one, I actually liked this part.
Start by defining backpressure and queue overflow, then explain strategies like bounded queues, load shedding, and rate limiting. For identical inputs, discuss caching, deduplication, and request coalescing to optimize performance.
Pro tip: Emphasize that backpressure is about graceful degradation and system stability, not just preventing crashes. Mention that deduplication must consider idempotency and cache invalidation to avoid stale results.
Explain what backpressure and queue overflow are, and why they occur in distributed systems. Mention that backpressure signals upstream to slow down, while overflow happens when queues exceed capacity.
Describe techniques like bounded queues, blocking producers, rate limiting, and load shedding. Highlight the importance of monitoring and adaptive throttling.
Discuss approaches such as dropping requests (with priority), spilling to disk, or scaling consumers. Mention trade-offs between latency and throughput.
Explain caching (e.g., memoization, Redis), request deduplication (e.g., using a hash of inputs), and request coalescing (e.g., singleflight). Note the need for cache invalidation and TTL.
Provide a concrete example, such as an API gateway with rate limiting and a cache for idempotent GET requests, to tie concepts together.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partial batch failure is the one that trips people up.
Structure your answer by first categorizing the failure modes (partial batch failures, model server crashes, replica scaling behavior) and then for each, describe detection, mitigation, and recovery strategies. Emphasize how these failures interact and the importance of designing for graceful degradation and idempotency. Conclude with how you would validate resilience through testing and monitoring.
Pro tip: Demonstrate maturity by discussing trade-offs between consistency and availability, and how you would prioritize failure modes based on user impact and business requirements. Mention specific techniques like circuit breakers, retries with exponential backoff, and health checks to show practical experience.
List the specific failure modes mentioned: partial batch failures, model server crashes, and replica scaling behavior. For each, clarify what constitutes a failure and its potential impact on the system.
Explain how you would detect each failure mode early, using metrics, logs, and alerts. For example, track batch job success rates, server health checks, and replica utilization.
Describe strategies to handle each failure: for partial batch failures, use idempotent processing and dead-letter queues; for server crashes, use replication and automatic failover; for scaling, use autoscaling with graceful shutdown and warm-up.
Discuss how these failure modes can compound (e.g., a crash during scaling) and the trade-offs between consistency, availability, and latency. Highlight design principles like idempotency and backpressure.
Explain how you would test resilience through chaos engineering, load testing, and failure injection. Emphasize continuous improvement based on post-mortems and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Per-request tracing and batch fill rate were the two I led with.
Start by clarifying the system's purpose and critical user journeys, then propose a layered observability strategy covering metrics, logs, and traces. Prioritize metrics that directly measure user experience and system health, and explain how you'd use them to detect and diagnose issues.
Pro tip: Tie every metric to a user-facing outcome or SLO, and mention that you'd start with a minimal set to avoid alert fatigue, iterating based on incidents and business needs.
Ask questions to understand the system's architecture, user base, and key performance indicators. Identify what 'healthy' looks like from both user and business perspectives.
Outline how you'd instrument metrics, logs, and traces, and how they complement each other. Mention tools like Prometheus, Grafana, Jaeger, or OpenTelemetry.
Select metrics that matter most: latency, error rates, throughput, saturation, and user-centric metrics like conversion or engagement. Explain why each is critical.
Propose service-level objectives and error budgets, and describe how you'd configure actionable alerts that minimize noise.
Emphasize that observability is iterative: start simple, learn from incidents, and refine metrics and dashboards over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Hot-swap I tied back to the routing layer from earlier, blue-green style with a gradual traffic shift.
Start by clarifying requirements and constraints, then propose a modular architecture with a model registry, routing layer, and autoscaling policies. Discuss trade-offs between consistency, latency, and cost, and emphasize observability and gradual rollouts.
Pro tip: Highlight the importance of versioned model artifacts and immutable deployments to enable safe rollbacks and reproducible experiments. Mention that A/B tests should be driven by business metrics, not just technical ones.
Ask about expected traffic volume, latency SLAs, model update frequency, and success metrics for A/B tests. This ensures your design addresses real needs.
Propose a model registry that stores versioned artifacts with metadata. Use a service mesh or API gateway to route requests to different model versions dynamically.
Describe a traffic splitting mechanism (e.g., weighted routing) and a framework for assigning users to variants consistently. Ensure metrics collection and analysis pipelines are in place.
Outline autoscaling based on traffic patterns using metrics like QPS, latency, and queue depth. Consider predictive scaling for known patterns and reactive scaling for spikes.
Discuss trade-offs between cost, performance, and complexity. Emphasize logging, monitoring, and alerting to detect issues during rollouts and scaling events.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.