Design file-embedding storage system

Last updated: Mar 29, 2026

Quick Overview

This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design file-embedding storage system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Design file-embedding storage system

Adobe

Aug 4, 2025, 10:55 AM

hardSoftware EngineerTechnical ScreenML System Design

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Design file-embedding storage system

System Design: Multimodal Embedding Service for User Uploads

Context

You are designing a backend service that, for each user-uploaded asset, generates vector embeddings and stores them for search and retrieval.

Supported asset types: documents (PDF/DOCX/TXT), images (PNG/JPEG), and videos (MP4).
Maximum upload size per asset: x MB (configurable; assume x is a limit enforced by the API).
The system must cover: ingestion, preprocessing, model selection, scalability, storage schema, and retrieval.
Assume multi-tenant usage and that asynchronous processing is acceptable (embeddings ready within seconds to minutes).

Requirements

Ingestion: Validate and accept uploads, handle security checks, and persist raw assets.
Preprocessing: Extract text from documents, handle OCR for scanned pages, handle frames/transcripts for videos, and normalize images/videos.
Model Selection: Choose models for text, image, and video embeddings (and speech-to-text where needed); justify trade-offs.
Scalability: Design for horizontal scale, efficient GPU/CPU utilization, and cost control; ensure observability and fault tolerance.
Storage Schema: Define where raw assets, metadata, and vectors live, including partitioning and indexing choices.
Retrieval: Support text, image, or video queries that retrieve relevant documents/images/videos; include cross-modal search.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.
State explicit assumptions before making sizing or architecture decisions.
Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers

A scoped requirements summary with concrete non-goals and success metrics.
ML-specific data, model, evaluation, serving, and monitoring choices.
Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.
A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions

What breaks first at 10x traffic or data volume?
How would you degrade gracefully during dependency failures?
What metrics and alerts would prove the design is healthy after launch?

Submit Your Answer to Earn 20XP

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