Roblox interview questions

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.

Treat it as multi-label, since games often fit several genres. Clean labels first: creator tags are noisy, so audit a sample and flag likely errors with confident learning.

Encode text with a pretrained text encoder, thumbnails with a CLIP-style image encoder, and keep tabular metadata as features, then late-fuse them in a small classifier with per-genre thresholds. Score in batch on updates. Monitor per-genre precision on audited samples, and drift as new genres appear.

Decode and resample the audio, then compute log-mel spectrograms over short overlapping windows. A keyword-spotting model, a small CNN or a pretrained audio encoder, scores each window; smooth over time and merge adjacent hits into events.

Calibrate scores on held-out data, for example with temperature scaling, then set per-keyword thresholds from precision targets. Send the uncertain band, plus a random sample of confident hits, to human labellers with clear guidelines and agreement checks. Their labels retrain the model and reset the thresholds.