Architect a geo-distributed Task Scheduling Engine
Last updated: December 22, 2025
Quick Overview
Design a geo-distributed task scheduling system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Plaid
December 22, 2025139
6
585 solved
Design a geo-distributed task scheduling system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Plaid goes beyond model selection. This Onsite question evaluates your ability to design end-to-end ML pipelines, from data collection to model serving, while considering production constraints like latency and reliability.
What the Interviewer Expects
- Define clear ML objectives with appropriate loss functions and metrics
- Design a comprehensive feature engineering pipeline
- Discuss model selection with trade-offs (complexity vs interpretability vs latency)
- Plan online and offline evaluation strategies including A/B testing
- Address serving infrastructure: batch vs real-time, latency requirements
- Consider data quality, labeling strategy, and feedback loops
Key Topics to Cover
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 debug a model that works well offline but poorly online?
- How would you run A/B tests on different model versions?
- How would you ensure fairness and reduce bias in the model?
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Requirements
Functional Requirements
- Task Scheduling: Ability to schedule, execute, and monitor millions of tasks across geo-distributed regions.
- Dynamic Scaling: Automatically scale resources ba...
Capacity Estimation
Based on a peak load of 10 million tasks per minute:
- Tasks per second: 10 million / 60 = ~166,667 tasks per second.
- Assuming an average task requires 100ms to schedule and execute, this means ...
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