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

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Jul 2026

Summary

Meta system design round, one massive question that basically covered every enterprise ML concern you can think of. Security, ethics, compliance, cost, monitoring, the whole thing crammed into one prompt. Left feeling like I touched everything but nailed nothing.

Questions Asked (1)

Design an enterprise ML data and inference platform that satisfies strict security and ethics requirements, covering multi-tenant isolation, PII handling, secret management, model governance with approval gates and bias audits, compliance with regulations like GDPR and SOC 2, SLOs for training and serving, and an end-to-end architecture with explicit trade-offs.

System DesignTechnical Trade-offsData Modeling

Author's notes

This question is basically six interviews in one.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then present a layered architecture that addresses security, governance, and SLOs. Walk through each layer, highlighting trade-offs and how they satisfy compliance and ethical needs. Conclude with a summary of key decisions and their implications.

Pro tip: Emphasize that security and governance are not afterthoughts but must be baked into every layer from the start, and proactively discuss trade-offs like latency vs. auditability to show engineering maturity.

1. Clarify Requirements and Constraints

Ask questions to understand scale, data types, regulatory scope, and existing infrastructure. Define clear SLOs for training and serving, and identify key stakeholders.

2. Design Multi-Tenant Isolation and PII Handling

Propose logical or physical isolation per tenant, with encryption at rest and in transit. Implement PII detection, anonymization, and access controls to ensure data privacy.

3. Implement Secret Management and Model Governance

Use a centralized secret manager with rotation and least privilege. Establish model approval gates, bias audits, and versioning to ensure ethical and compliant model deployment.

4. Ensure Compliance and Define SLOs

Map controls to GDPR and SOC 2 requirements, including data subject rights and audit logging. Define and monitor SLOs for training (e.g., job completion time) and serving (e.g., latency, availability).

5. Present End-to-End Architecture and Trade-offs

Describe the full pipeline from data ingestion to serving, highlighting components like feature store, orchestration, and monitoring. Discuss trade-offs such as isolation vs. cost, and auditability vs. performance.

Key Points to Mention

Multi-tenant isolation strategies (namespace, VPC, dedicated instances) and their trade-offs
PII detection, anonymization, and encryption techniques (e.g., tokenization, differential privacy)
Secret management best practices (e.g., HashiCorp Vault, AWS Secrets Manager) with rotation and audit
Model governance: approval workflows, bias detection, explainability, and audit trails
Compliance frameworks: GDPR (data subject rights, consent) and SOC 2 (security, availability, confidentiality)
SLOs for training (throughput, cost) and serving (latency, error rate) with monitoring and alerting

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