Senior interview questionAsked at Roblox · interview write-up
Design the architecture of a real-time game recommender for millions of games and tens of millions of users, with request flow and latency SLOs.
Model answer
The client calls a recommendation service, which fetches user features and recent sessions, then retrieves candidates from several sources: a two-tower ANN index over millions of games, trending games, and friends' games. It ranks a few hundred with a multi-task model and applies filters and diversity before returning.
Set an end-to-end latency SLO and split it by stage, with timeouts and a cached fallback list. Stream play events into the feature store so recent sessions count within minutes, retrain daily, and refresh the item index as games launch.
Practise the rest
40 ML system design questions, 492 in all, each with a model answer. Free.
More from Roblox interview questions.
More ML system design interview questions
- You need to copy 500 GB of weights to 1,000 GPU workers over shared 10 Gbps links. What's the lower bound on time, and what would you build?
- Design an LLM coding assistant end to end: data, model and evaluation.
- Design a job scheduler for ML training and batch inference on a shared GPU cluster.
- Training a retrieval model over a catalogue of millions of items, you use sampled softmax. Why does it need a correction, and how do you choose the classification loss?
- Design an inference API that converts PDFs into Markdown using several models in stages.
- How can LLMs be incorporated into recommendation systems, from metadata enrichment and cold start to generative retrieval and semantic reranking?