Meta·Software Engineer·Onsite - System Design / Architecture·Senior

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Apr 2026

Summary

Meta system design round focused entirely on one deep question about API design and validation strategy for a top-k frequency service. Dense, technical, and they clearly wanted to see how far you could go before running out of things to say.

Questions Asked (1)

Design the API and system-level input validation and error-handling strategy for a service that computes the top-k most frequent elements in an array. Cover the request/response schema, payload limits, k bounds, element domains, idempotency, auth, rate limiting, timeouts, error taxonomy, validation layers, partial-failure behavior, observability, and safeguards against malformed or poisoned payloads. Also describe how you'd test and monitor this in production.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

This question is basically six questions stapled together and they let you run with it for the whole session.

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AI HintsAI Generated

Suggested Approach

Structure your answer around the request lifecycle: define the API contract, then layer validation and error handling from the edge to the core algorithm. Emphasize defense-in-depth with multiple validation layers and a clear error taxonomy. Conclude with testing and monitoring strategies to ensure reliability and observability.

Pro tip: Show maturity by discussing trade-offs between strict validation and flexibility, and by proposing idempotency keys and circuit breakers to handle downstream failures gracefully.

1. Define API Contract and Constraints

Specify the request/response schema, payload size limits, k bounds, and element domain. Include idempotency, auth, and rate limiting requirements.

2. Design Validation Layers

Outline validation at the edge (API gateway), service layer, and algorithm level. Cover syntactic, semantic, and business rule validation.

3. Establish Error Taxonomy and Handling

Define error categories (client, server, transient), HTTP status codes, error response format, and partial-failure behavior. Include timeouts and retries.

4. Implement Observability and Safeguards

Describe logging, metrics, tracing, and alerting. Add safeguards against malformed/poisoned payloads (e.g., schema validation, sanitization, circuit breakers).

5. Plan Testing and Monitoring

Detail unit, integration, load, and chaos testing. Specify production monitoring dashboards, SLOs, and anomaly detection.

Key Points to Mention

Idempotency keys to ensure safe retries and exactly-once processing.
Rate limiting per user/API key with token bucket or sliding window algorithms.
Error taxonomy with consistent error codes and messages for client vs. server errors.
Partial failure handling: return partial results with warnings if some elements are invalid, or fail fast based on configurable policy.
Observability: structured logging, metrics (latency, error rates, k distribution), and distributed tracing.
Testing: property-based testing for edge cases, fuzzing for malformed inputs, and load testing for performance under scale.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.