Lyft·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

System design round at Lyft for an MLE role, and they threw a full RAG platform question at me for a bank's HR org. Not what I expected from a ride-share company but the question itself was genuinely interesting to work through.

Questions Asked (1)

Design an internal AI platform for a bank's HR department that ingests policy documents, benefits guides, and compliance materials, lets employees ask natural-language questions, and returns answers with citations. Cover functional requirements, security, document ingestion, retrieval design, model serving, access control, evaluation, monitoring, and scaling.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This was basically a full system design interview wrapped around a RAG use case.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the core requirements and constraints, then propose a high-level architecture that addresses each area: ingestion, retrieval, serving, security, and monitoring. Emphasize trade-offs and justify your choices based on the bank's regulatory environment and HR use case. Conclude by discussing evaluation and scaling strategies.

Pro tip: Highlight the importance of data privacy and compliance from the start, as banks have strict regulations. Also, suggest a phased rollout starting with a pilot to gather feedback and iterate.

1. Clarify Requirements and Constraints

Ask questions to understand the scale, types of documents, user roles, compliance requirements, and expected query volume. This ensures the design meets actual needs.

2. Design Document Ingestion and Processing Pipeline

Outline how documents are ingested, parsed, chunked, embedded, and indexed. Consider handling various formats and updating documents.

3. Architect Retrieval and Model Serving

Describe the retrieval mechanism (e.g., vector search) and how the language model generates answers with citations. Discuss model selection, fine-tuning, and serving infrastructure.

4. Implement Security and Access Control

Detail authentication, authorization, encryption, and audit logging. Ensure role-based access to documents and queries.

5. Plan Evaluation, Monitoring, and Scaling

Define metrics for accuracy and user satisfaction, set up monitoring for performance and drift, and design for horizontal scaling.

Key Points to Mention

Use of retrieval-augmented generation (RAG) to provide accurate, cited answers.
Vector database for efficient similarity search over document embeddings.
Role-based access control (RBAC) and encryption to protect sensitive HR data.
Evaluation metrics like answer relevance, citation accuracy, and user feedback.
Monitoring for model drift, latency, and system health.
Scaling strategies: sharding, caching, and load balancing.

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