Design a Load Balancing for Goldman Sachs
Last updated: May 11, 2026
Quick Overview
Design a low-latency load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Goldman Sachs
May 11, 202649
4
3,930 solved
Design a low-latency load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Goldman Sachs 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
- Map the business problem to a concrete ML objective
- Propose reasonable features and a baseline model
- Discuss basic model evaluation metrics
- Outline a simple serving architecture
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 handle a 10x increase in prediction requests?
- How would you run A/B tests on different model versions?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For Load Balancing for Goldman Sachs, key functional requirements include...
Capacity Estimation
Estimate the scale to drive design decisions. Assume 100M DAU with an average of 10 actions per user per day = 1B requests/day ~ 12K QPS average, ~36K...
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