Scale AI·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

Scale AI system design round for a SWE role. The whole interview was one big open-ended problem: build an insurance claims agent using RAG, pick your infra and storage, and keep token costs from spiraling out of control. Dense prompt, lots of moving parts.

Questions Asked (1)

Design an AI agent system for processing insurance claims end-to-end: it ingests incoming claims, uses retrieval-augmented generation to pull relevant context, and outputs an approval decision. Walk through your architecture including storage choices and how you'd keep LLM token costs under control.

System DesignTechnical Trade-offsData Modeling

Author's notes

This one sprawled fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then present a high-level architecture with clear components: ingestion, RAG pipeline, decision engine, and storage. Emphasize trade-offs in storage choices (e.g., vector DB vs. relational) and cost-control techniques like caching, batching, and model selection. Conclude with a discussion of evaluation and monitoring for accuracy and cost.

Pro tip: Quantify the impact of cost-saving measures—e.g., 'Caching reduces token usage by 30%'—to show you think in terms of business metrics, not just technical features. Also, mention that you'd start with a simple baseline (e.g., keyword search) and iterate, demonstrating pragmatism.

1. Clarify Requirements and Scale

Ask about claim volume, latency requirements, accuracy targets, and budget constraints. This shapes architectural decisions like batch vs. real-time processing and model choice.

2. Design the End-to-End Pipeline

Outline components: ingestion (API/queue), preprocessing (OCR, normalization), RAG (retriever + generator), decision logic (rules + LLM), and output (approval/denial with explanation).

3. Choose Storage Solutions

Select storage for different data types: vector DB for embeddings, relational DB for structured claim data, object storage for documents, and a cache for frequent queries.

4. Implement Cost Controls

Describe techniques: caching embeddings and responses, batching requests, using smaller models for simple tasks, prompt compression, and fallback to rules when confidence is high.

5. Discuss Evaluation and Monitoring

Explain how to measure accuracy (human-in-the-loop, A/B tests) and track token usage/cost per claim. Mention logging and alerting for anomalies.

Key Points to Mention

Use of a vector database (e.g., Pinecone, Weaviate) for efficient retrieval of relevant policy documents and past claims.
Hybrid retrieval combining keyword search (BM25) and semantic search for better recall.
Caching strategies: embedding cache, response cache, and semantic caching to avoid redundant LLM calls.
Model tiering: use smaller/cheaper models (e.g., GPT-3.5) for simple classification and larger models only for complex cases.
Batch processing of claims to amortize LLM costs and improve throughput.
Human-in-the-loop for low-confidence decisions to ensure accuracy and provide feedback for fine-tuning.

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