Lyft·AI Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

System design round at Lyft for an AI Engineer role, basically one big question about building an internal HR Q&A platform on top of company docs. Dense question with a lot of moving parts, felt like they wanted to see how far you could go end-to-end.

Questions Asked (1)

Design an AI platform that lets employees ask natural-language questions about internal HR documentation. Walk through the full pipeline from document ingestion to answer generation, including retrieval, access control, PII handling, and evaluation.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This one sprawled in every direction.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., document types, user roles, compliance needs), then walk through the end-to-end pipeline in logical stages: ingestion, indexing, retrieval, access control, PII handling, and answer generation. Emphasize trade-offs at each stage, such as retrieval accuracy vs. latency, and how you would evaluate the system with both automated metrics and human feedback.

Pro tip: Proactively discuss how you would handle access control at the retrieval layer (e.g., filtering documents by user permissions before retrieval) to avoid leaking sensitive information, and mention the importance of auditing and logging for compliance.

1. Clarify Requirements and Constraints

Ask about document types (policies, handbooks), user roles, expected query volume, latency requirements, and compliance regulations (e.g., GDPR, HIPAA). This shapes design decisions.

2. Design Ingestion and Indexing Pipeline

Outline how documents are ingested (batch/stream), parsed (PDF, HTML), chunked, embedded, and stored in a vector database with metadata (e.g., department, access level).

3. Implement Retrieval with Access Control and PII Handling

Describe hybrid retrieval (keyword + semantic) with permission filters applied at query time. Explain PII detection and redaction (e.g., using NER or regex) before indexing or at retrieval.

4. Generate Answers with LLM and Guardrails

Use retrieved context to prompt an LLM, ensuring answers are grounded. Include guardrails like citation of sources, fallback to 'I don't know', and post-processing for PII leakage.

5. Evaluate and Monitor System Performance

Define metrics (retrieval recall, answer accuracy, latency) and evaluation methods (offline benchmarks, A/B tests, user feedback). Set up monitoring for drift and failures.

Key Points to Mention

Hybrid retrieval combining sparse (BM25) and dense (embeddings) methods for better accuracy.
Access control enforcement at the retrieval layer using document-level permissions and user role mapping.
PII detection and redaction techniques (e.g., named entity recognition, regex) applied during ingestion or retrieval.
Use of LLM with retrieval-augmented generation (RAG) and guardrails to prevent hallucinations and ensure grounded answers.
Evaluation metrics: retrieval precision/recall, answer correctness (e.g., via human eval or LLM-as-judge), and latency.
Trade-offs: latency vs. accuracy, cost of embedding vs. keyword search, and privacy vs. utility.

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