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

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Jun 2026

Summary

Scale AI system design round for a software engineer role. The whole thing was one big open-ended problem about building a document processing pipeline on top of black-box ML services. Pretty involved for a single session.

Questions Asked (1)

Design an end-to-end pipeline that accepts user file uploads (single or bulk, up to 1,000 files), runs each document through a black-box classification service and a black-box embedding service, stores the results, and supports querying by user, file, or semantic similarity. The system must handle low-latency single uploads and high-throughput bulk uploads.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

This is one of those questions where the scope keeps expanding the more you talk.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a scalable, decoupled architecture that handles both low-latency single uploads and high-throughput bulk uploads. Focus on asynchronous processing, idempotency, and efficient storage/querying for classification and embedding results.

Pro tip: Emphasize idempotency and backpressure handling for bulk uploads, and propose a tiered storage strategy (hot vs. cold) to balance latency and cost. This shows you think about real-world operational concerns beyond just functional requirements.

1. Clarify Requirements and Constraints

Ask about expected file sizes, types, latency SLAs, throughput targets, consistency needs, and budget. Confirm that classification and embedding services are black-box with unknown latency and rate limits.

2. High-Level Architecture

Propose a decoupled pipeline: API gateway for uploads, object storage for raw files, a message queue for asynchronous processing, workers that call the black-box services, and a database for metadata and results. Include a separate indexing service for embeddings.

3. Handling Single vs. Bulk Uploads

For single uploads, process synchronously or with low-latency async (e.g., direct call to services with caching). For bulk, use batch processing with parallel workers, rate limiting, and progress tracking. Ensure idempotency via unique file IDs.

4. Storage and Querying

Store metadata (user, file, status) in a relational DB, classification results in a document store or wide-column DB, and embeddings in a vector database. Support queries by user, file ID, and semantic similarity via vector search.

5. Scalability, Reliability, and Trade-offs

Discuss horizontal scaling of workers, backpressure, retries with exponential backoff, dead-letter queues, and monitoring. Trade-offs: latency vs. throughput, cost vs. performance, consistency vs. availability.

Key Points to Mention

Asynchronous processing with message queues (e.g., Kafka, SQS) for bulk uploads to decouple ingestion from processing.
Idempotency and deduplication using file hashes or unique IDs to handle retries and duplicate uploads.
Vector database (e.g., Pinecone, Weaviate) for semantic similarity search, and indexing strategies for efficient retrieval.
Rate limiting and backpressure to avoid overwhelming black-box services, with circuit breakers for resilience.
Caching of classification and embedding results for frequently uploaded or similar files to reduce latency and cost.
Monitoring and observability: track queue depths, processing times, error rates, and service quotas.

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