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

SeniorPrefer not to say

Jun 2026Remote

Summary

System design round at ClickUp for a software engineer role. The whole session was basically one big question about building a weekly email digest feature, and the rate-limited ML scoring service was the real crux of it. Trickier than I expected.

Questions Asked (1)

Design a system that sends each post owner a weekly email digest of their top comments, where comments are scored by a rate-limited ML service.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

I jumped straight into the happy path and started sketching a batch job that fires off all ML scoring requests on Sunday night.

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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 a rate-limited ML service, and aggregates top comments per user for weekly email delivery. Emphasize trade-offs around rate limiting, data freshness, and system reliability, and discuss how to handle failures and retries.

Pro tip: Proactively address the rate-limited ML service by proposing a queue-based architecture with backpressure and caching, and discuss how to prioritize scoring for active users to stay within limits while maintaining digest quality.

1. Clarify Requirements and Scale

Ask about expected number of users, comments per week, ML service rate limits, and digest personalization needs. Define what 'top comments' means (e.g., by score, recency) and email frequency.

2. High-Level Architecture

Outline components: comment ingestion, storage, ML scoring service (rate-limited), aggregation, and email delivery. Sketch data flow from comment creation to weekly digest.

3. Design ML Scoring Pipeline with Rate Limiting

Propose a queue (e.g., Kafka, SQS) to buffer comments for scoring. Implement rate limiting via token bucket or leaky bucket, and use caching to avoid re-scoring. Discuss prioritization (e.g., score comments for users who will receive digest soon).

4. Aggregation and Digest Generation

Design a batch job (e.g., weekly cron) that queries scored comments, groups by post owner, selects top N, and generates personalized email content. Consider pre-computing aggregates to reduce load.

5. Reliability, Scalability, and Trade-offs

Address failure handling (retries, dead-letter queues), idempotency, and monitoring. Discuss trade-offs: latency vs. freshness, cost of ML scoring, and potential for stale data if rate limits are hit.

Key Points to Mention

Rate limiting strategies (token bucket, leaky bucket) and how to integrate with ML service
Queue-based decoupling to handle bursts and backpressure
Caching of ML scores to avoid redundant calls and stay within limits
Prioritization of comments for scoring based on user activity or digest schedule
Batch processing for weekly digest generation and email delivery
Trade-offs between real-time scoring and batch processing, and handling of stale data

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