clickup·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Jul 2026Remote

Summary

System design round at Clickup for a software engineer role. The whole interview was one big design question about a weekly email digest system, which sounds straightforward until you actually start pulling on the threads around rate limiting and deduplication.

Questions Asked (1)

Design a service that sends a weekly email digest of the top comments on each post to the post owner, where comment quality is scored by a rate-limited external ML service.

System DesignData ModelingTechnical Trade-offs

Author's notes

I started with the happy path and immediately got pushed on the ML rate limiting piece.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a pipeline that ingests comments, scores them via the rate-limited ML service with batching and caching, and stores results for weekly digest generation. Focus on trade-offs around rate limiting, data freshness, and delivery reliability, and explain how you'd handle failures and scale.

Pro tip: Proactively discuss how to handle the rate limit: use a queue with backoff, batch comments per post, and cache scores to avoid re-scoring. Also mention idempotency and deduplication to prevent duplicate emails.

1. Clarify Requirements and Scale

Ask about scale (number of posts, comments per post, users), freshness (weekly digest based on comments from the past week), and quality threshold. Confirm that the ML service is rate-limited and external.

2. High-Level Architecture

Propose a pipeline: comment ingestion -> queue -> scoring workers -> score storage -> digest generator -> email sender. Use a database to store comments and scores, and a scheduler for weekly runs.

3. Handling Rate-Limited ML Service

Design a rate limiter and queue to control requests to the ML service. Use batching, caching, and exponential backoff on 429s. Consider scoring only new comments and reusing scores.

4. Data Modeling and Digest Generation

Model comments with post_id, user_id, timestamp, score, and status. For each post owner, query top-scored comments from the past week, group by post, and generate an email digest.

5. Reliability and Trade-offs

Discuss failure handling: retries, dead-letter queues, idempotent email sending. Trade-offs: latency vs. freshness, cost of ML calls, and complexity of real-time vs. batch scoring.

Key Points to Mention

Rate limiting strategies: token bucket, leaky bucket, and queue-based backpressure.
Caching ML scores to avoid redundant calls and reduce cost.
Data model: comments table with score, post_id, user_id, and timestamp; index for efficient weekly queries.
Scheduling: cron or workflow engine (e.g., Airflow) for weekly digest generation.
Email delivery: use a service like SendGrid, handle bounces, and ensure idempotency.
Trade-offs: batch vs. real-time scoring, freshness vs. cost, and scalability of the ML service.

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