← Anthropic Interview Insights
I spent too long on the model side before realizing they explicitly didn't care about that.
Start by clarifying the workload characteristics (model size, latency SLOs, traffic patterns) and then walk through a layered scaling strategy: autoscaling policies, hardware placement, multi-region architecture, and capacity planning. Emphasize trade-offs between cost, latency, and reliability, and tie decisions back to concrete metrics and SLOs.
Pro tip: Anchor your answer in SLOs and cost-per-inference, and mention that you'd validate scaling decisions with load tests and canary deployments before full rollout. This shows you think about production readiness, not just theoretical scaling.
Ask about model size, latency/throughput SLOs, traffic variability, budget, and compliance/regional constraints. This ensures your scaling design is grounded in real needs.
Propose horizontal pod autoscaling based on custom metrics (e.g., queue depth, GPU utilization, request latency) and discuss predictive scaling for known traffic patterns. Mention the need for scale-to-zero for cost savings where latency permits.
Explain that GPU is for compute-heavy inference (large models, low latency), while CPU is for lightweight models or preprocessing. Discuss mixed-node clusters, GPU sharing (MIG, time-slicing), and fallback to CPU during GPU shortages.
Cover active-active vs active-passive, data replication, model artifact distribution, and routing via global load balancers. Address latency, failover, and data sovereignty.
Describe forecasting based on historical trends, headroom for spikes, and reserved vs spot instances. Include monitoring, load testing, and iterative tuning to balance cost and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by categorizing metrics into system health, model performance, and business impact, then define SLOs for each with clear error budgets. Explain how you'd set up alerting on SLO violations and drift detection using statistical tests and monitoring pipelines. Emphasize a layered approach that balances proactive and reactive measures.
Pro tip: Tie every metric to a user-facing or business outcome to avoid vanity metrics, and always include a plan for alert fatigue by prioritizing alerts based on severity and actionability.
Identify three key categories: system metrics (latency, throughput, error rates), model metrics (accuracy, precision, recall, F1, calibration), and business metrics (conversion, revenue, user engagement).
For each critical metric, define a target SLO (e.g., 99.9% availability, p95 latency < 200ms, model accuracy > 95%) and an error budget that dictates acceptable failure rates.
Set up alerts for SLO violations with severity levels, using tools like Prometheus and Alertmanager. Include runbooks for common issues and avoid alert fatigue by grouping and deduplicating alerts.
Monitor data drift (input feature distributions) and concept drift (relationship between features and target) using statistical tests (e.g., KS test, PSI) and windowed comparisons. Trigger retraining or investigation when drift exceeds thresholds.
Integrate monitoring with CI/CD pipelines for automated retraining, and use canary deployments to validate model updates before full rollout.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem around user-facing SLOs (latency, throughput, cost) and then walk through a layered optimization strategy: batching, caching, model-level techniques (quantization/distillation), and load shedding. Emphasize tradeoffs and how you measure and iterate, showing a systematic approach rather than a one-size-fits-all solution.
Pro tip: Quantify the impact of each technique with concrete metrics (e.g., 'batching reduced p99 latency by 30% but increased tail latency under bursty load') and mention how you validate tradeoffs with A/B tests or shadow deployments. This demonstrates you think in terms of measurable outcomes, not just theoretical optimizations.
Clarify the performance goals (latency, throughput, cost) and constraints (hardware, model size, accuracy requirements). Establish baseline metrics to measure improvements.
Implement dynamic batching to maximize hardware utilization while respecting latency SLOs. Use caching (e.g., KV cache, embedding cache, result cache) to avoid redundant computation.
Evaluate quantization (e.g., FP16, INT8) and distillation to reduce model size and compute. Assess accuracy-latency tradeoffs and choose techniques that meet quality bars.
Under overload, shed low-priority requests or degrade gracefully (e.g., shorter max tokens). Combine with autoscaling to handle traffic spikes while maintaining SLOs.
Continuously monitor key metrics (p50/p99 latency, throughput, error rates) and iterate on optimizations. Use canary deployments to safely test changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They gave me the choice of which failure scenario to walk through and I picked latency spike because I'd actually debugged one before.
Start by clarifying the symptom and scope—when it started, which metrics, and whether it's latency or accuracy—then systematically narrow down from the highest-level signals to the root cause using a structured debugging framework. Emphasize observability, hypothesis-driven investigation, and safe rollback or mitigation before deep fixes.
Pro tip: Always check recent changes first—deployments, model updates, data pipeline changes—since most regressions are caused by something that changed, not by mysterious system behavior. Also, quantify the impact and establish a baseline before diving deep.
Clarify the exact symptom: is it p99 latency spike, accuracy drop, or both? Determine when it started, which segment (e.g., specific model, endpoint, user cohort), and the magnitude. Establish a baseline from before the issue.
Review recent deployments, model retraining, data pipeline changes, config updates, or infrastructure changes. Correlate the timeline of changes with the onset of the issue using dashboards and logs.
Use metrics, logs, and traces to isolate the bottleneck or degradation. For latency, break down by component (e.g., preprocessing, model inference, postprocessing) and check resource utilization. For accuracy, slice metrics by data segments and compare prediction distributions.
Based on evidence, form specific hypotheses (e.g., data drift, model staleness, resource contention, bug in feature pipeline). Test each with targeted experiments, such as A/B comparisons, shadow deployments, or offline evaluation on recent data.
Apply immediate mitigation (e.g., rollback, scale resources, fallback model) to restore service. Then implement long-term fixes and add monitoring/alerting to catch similar issues earlier.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.