Architect a low-latency Task Scheduling Engine
Last updated: December 28, 2025
Quick Overview
Design a low-latency task scheduling system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Notion
System Design
Software Engineer
Notion
December 28, 2025Software Engineer
System Design Round
System Design
Hard
8
5
1,914 solved
Design a low-latency task scheduling system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Notion asks this during the System Design Round to assess your understanding of the full ML lifecycle. They want to see how you translate a business problem into an ML objective, design the feature pipeline, and plan for model monitoring and retraining.
What the Interviewer Expects
- Design the full ML lifecycle from data collection to model monitoring
- Address cold start, exploration/exploitation, and model freshness
- Discuss multi-objective optimization and ranking systems
- Plan for model debugging, fairness, and bias mitigation
- Design the feature store and training pipeline for scale
- Address model versioning, canary deployments, and rollback strategies
- Discuss the data flywheel and long-term system evolution
Key Topics to Cover
ML objective formulation and metric selection
Feedback loops and model retraining
Training pipeline and infrastructure
A/B testing and experimentation
Model serving and latency optimization
Monitoring and model degradation detection
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you ensure fairness and reduce bias in the model?
- How would you debug a model that works well offline but poorly online?
- How would you run A/B tests on different model versions?
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Requirements
- Functional Requirements:
- The system should support scheduling millions of tasks with low latency (sub-100ms).
- Ability to prioritize tasks based on user-defined rules, urgency, and system...
Capacity Estimation
To estimate capacity:
- Assume 10 million users, each making an average of 5 task scheduling requests per day.
- Total requests per day = 10 million * 5 = 50 million.
- If we assume a peak load ...
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