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

SeniorPrefer not to say

May 2026

Summary

System design round at OpenAI for an ML Engineer role. The whole thing was basically one giant question about RAG systems, which sounds manageable until you realize they want you to cover everything from chunking strategy to access control to horizontal scaling in one coherent answer.

Questions Asked (1)

Design a full end-to-end RAG search system for enterprise users. Walk through the architecture, how data gets ingested, how you'd pick a retriever and generator, indexing strategy, latency constraints, security considerations, and how the system scales.

System DesignTechnical Trade-offs

Author's notes

This is a beast of a question and I underestimated how much the enterprise context changes things.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (data sources, scale, latency, security) and then present a layered architecture covering ingestion, indexing, retrieval, generation, and serving. Emphasize trade-offs at each layer, justify choices based on enterprise constraints, and discuss scaling and security throughout.

Pro tip: Anchor your design around measurable SLOs (e.g., p95 latency < 2s) and explicitly call out how each component contributes to meeting them, showing you think like a production engineer.

1. Clarify Requirements and Constraints

Ask about data volume, sources, query patterns, latency targets, security/compliance needs, and scale expectations. This shapes all subsequent design decisions.

2. Design Ingestion and Indexing Pipeline

Outline how data is ingested (batch/stream), preprocessed (chunking, embedding), and indexed (vector DB, hybrid indexes). Discuss incremental updates and metadata enrichment.

3. Select Retriever and Generator

Choose retrieval methods (dense, sparse, hybrid) and generation models (LLM) based on accuracy, latency, and cost. Explain how to combine them (e.g., retrieve-then-rerank-then-generate).

4. Address Latency, Security, and Scaling

Detail techniques to meet latency SLOs (caching, sharding, async), security measures (encryption, access control, PII redaction), and scaling strategies (horizontal scaling, load balancing).

5. Summarize Trade-offs and Monitoring

Recap key trade-offs (e.g., recall vs. latency, cost vs. quality) and mention monitoring, evaluation, and feedback loops for continuous improvement.

Key Points to Mention

Hybrid retrieval combining dense (embedding) and sparse (BM25) methods for better recall
Chunking strategies and their impact on retrieval quality and context length
Vector database selection (e.g., Pinecone, Weaviate, FAISS) and indexing algorithms (HNSW, IVF)
Latency optimization techniques: caching, approximate nearest neighbor search, model quantization, and async processing
Security: encryption at rest/in transit, RBAC, audit logging, and PII detection/redaction
Scaling: sharding, replication, load balancing, and auto-scaling for both retrieval and generation services

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