← Anthropic Interview Insights
I started with the polling contract because that felt like the core of it.
Start by clarifying requirements and constraints, then design the API schemas and job lifecycle, and finally address reliability concerns like idempotency, timeouts, retries, and rate limiting. Emphasize trade-offs and scalability throughout, and tie back to how this supports Anthropic's AI inference workloads.
Pro tip: Proactively discuss how you'd handle long-running jobs and partial failures, and mention using idempotency keys to make retries safe—this shows you've thought about real-world production issues.
Ask about expected job volume, latency SLAs, payload sizes, and whether results need to be stored or streamed. This ensures your design meets actual needs.
Define POST /jobs request/response and GET /jobs/{id} response, including job statuses (e.g., queued, processing, completed, failed, cancelled). Specify how clients poll and retrieve results.
Use idempotency keys for job submission to prevent duplicates. Design retry logic with exponential backoff and jitter for both clients and internal workers, and ensure operations are idempotent.
Set timeouts for job execution and polling, and implement rate limiting per client (e.g., token bucket) to protect the service. Discuss how to communicate limits via headers.
Explain how you'd scale the queue, workers, and storage, and trade-offs between polling frequency, latency, and cost. Mention monitoring and alerting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the inference service's requirements: expected request rate, latency SLOs, model size, and result retention needs. Then propose a decoupled architecture with a durable job queue (e.g., Redis or SQS), a horizontally scalable worker pool with autoscaling, and a tiered storage strategy (hot cache for intermediate results, object storage for final outputs).
Pro tip: Emphasize idempotency and exactly-once processing semantics—design workers to be stateless and use idempotency keys to avoid duplicate work, which is critical for cost control and correctness in ML inference pipelines.
Ask about request volume, latency targets, model size, and result retention. This determines queue choice, worker count, and storage tiers.
Choose a durable, distributed queue (e.g., Redis Streams, SQS, RabbitMQ) with visibility timeouts, dead-letter queues, and priority support if needed. Ensure at-least-once delivery and idempotent job processing.
Propose stateless workers that pull jobs, load models (cached in memory), and write results. Include autoscaling based on queue depth, health checks, and graceful shutdown.
Use a fast cache (e.g., Redis) for intermediate results with TTL, and durable object storage (e.g., S3) for final outputs. Consider a database for metadata and job status tracking.
Discuss retries with exponential backoff, dead-letter queues, and observability (metrics, logs, tracing) to ensure reliability and debuggability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Batching was the part I actually had opinions on.
Start by clarifying the workload characteristics (model size, request rate, latency SLOs) and then present a layered architecture: a scalable worker pool with autoscaling, dynamic batching to maximize throughput, and hardware-aware scheduling that routes to the best accelerator (GPU/TPU/CPU) for each task. Emphasize trade-offs between latency, cost, and utilization, and how you'd measure and iterate.
Pro tip: Quantify the impact of batching and hardware choice with concrete numbers (e.g., 'batching 8 requests can improve GPU utilization from 30% to 80%') and mention that you'd start with a simple solution and only add complexity when metrics justify it.
Ask about expected QPS, latency SLOs, model size, input variability, and budget. This ensures your design targets the right trade-offs.
Propose a pool of stateless workers behind a load balancer, with horizontal autoscaling based on queue depth or CPU/GPU utilization. Use a message queue for decoupling and backpressure.
Aggregate incoming requests into batches up to a max size or timeout, balancing latency and throughput. Use a batching layer that adapts to load and supports priority or deadline-aware scheduling.
Route inference to GPUs/TPUs when beneficial, using model quantization, compilation (e.g., TensorRT, XLA), and multi-stream/multi-model serving to maximize utilization. Consider CPU for small models or low-latency needs.
Instrument key metrics (latency percentiles, throughput, GPU utilization, cost per inference) and use them to tune batching parameters, autoscaling policies, and hardware allocation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partial failures in a batch are nasty and I said so.
Start by clarifying the batch job's scale, criticality, and existing infrastructure, then propose a layered design that separates observability, error handling, and recovery concerns. Emphasize idempotency, checkpointing, and dead-letter queues as core mechanisms, and tie your answer back to real-world trade-offs like cost, latency, and operational complexity.
Pro tip: Anchor your answer in concrete failure scenarios (e.g., a downstream API timeout mid-batch) and show how your design detects, contains, and recovers from them—interviewers at Anthropic value pragmatic root-cause thinking over buzzwords.
Ask about batch size, SLA, data sensitivity, and existing tooling to scope the solution appropriately. This ensures your design addresses the actual problem rather than a generic one.
Define metrics (success/failure counts, latency, throughput), structured logging with correlation IDs, and distributed tracing for batch stages. Include alerting thresholds and dashboards for real-time visibility.
Use try/catch per item or chunk, classify errors (transient vs. permanent), and apply retries with exponential backoff and jitter for transient failures. Route permanent failures to a dead-letter queue with context for later analysis.
Checkpoint progress after each successful chunk, make operations idempotent, and design for resumability from the last checkpoint. Consider compensating transactions or rollback strategies for non-idempotent side effects.
Simulate failures (e.g., kill a worker mid-batch) to test recovery, and review logs/metrics to identify gaps. Propose a feedback loop for continuous improvement based on incident post-mortems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.