Series B+ Startup·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Jun 2026

Summary

System design round for a software engineer role. The problem was a data pipeline with multiple filtering stages and some wrinkles around media storage and training data quality. Nothing too brutal, but there were enough moving parts to keep it interesting.

Questions Asked (1)

Design a backend pipeline where many clients send in data, the system runs it through a series of filters (upside-down image detection, duplicate action removal, spam filtering), and then stores it. Address how you'd handle large media files and bias in training data.

System DesignTechnical Trade-offsData Modeling

Author's notes

The core flow came together pretty quickly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a scalable, event-driven architecture with asynchronous processing. Address media handling via chunked uploads and object storage, and discuss bias mitigation through diverse data collection and regular audits.

Pro tip: Emphasize idempotency and backpressure to handle duplicate submissions and traffic spikes gracefully. Also, mention that bias mitigation is an ongoing process, not a one-time fix.

1. Clarify Requirements and Constraints

Ask about data volume, latency requirements, media types, and existing infrastructure to tailor the design. This shows you avoid over-engineering and focus on what matters.

2. Design High-Level Architecture

Propose an event-driven pipeline: clients upload to object storage (e.g., S3) and send metadata to an ingestion API, which enqueues messages for asynchronous processing. Use a message queue (e.g., Kafka) to decouple stages and enable scalability.

3. Detail Filtering Stages

For each filter, describe implementation: upside-down detection via pre-trained model, duplicate removal via hashing or similarity checks, spam filtering via ML or rule-based systems. Ensure filters are idempotent and can be retried.

4. Handle Large Media Files

Use chunked, resumable uploads directly to object storage with pre-signed URLs to offload from API servers. Process media asynchronously, possibly with dedicated workers and CDN for delivery.

5. Address Bias in Training Data

Discuss strategies: diverse data sourcing, bias detection metrics, regular audits, and human-in-the-loop for edge cases. Emphasize monitoring and feedback loops to continuously improve.

Key Points to Mention

Scalability and decoupling via message queues and microservices
Idempotency and exactly-once processing to handle duplicates and retries
Use of object storage and pre-signed URLs for large media
Asynchronous processing with backpressure to handle spikes
Bias mitigation techniques: diverse datasets, fairness metrics, and audits
Monitoring, logging, and alerting for pipeline health and bias detection

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