Reddit interview questions

Label: did the user comment within a fixed window after a real impression, one actually viewed, so unseen posts aren't false negatives.

Features, taken as of impression time: user history and community affinity; post age, community, text embedding and comment velocity; author affinity; context. Start with gradient-boosted trees, then a multi-task network that learns comments alongside clicks and votes, since comments are sparse. Evaluate log loss, calibration and AUC, then comments per user online.

One feature definition, in versioned code, compiled to both batch and streaming jobs. They write an append-only, timestamped offline store and a key-value online store holding the latest value per entity.

Training uses an as-of join: each label gets the latest feature value at or before its event time, never after.

For consistency, log served features and compare them with offline recomputation to catch skew. CI tests transforms, schemas and backfills. In production, replicate the online store, give every feature a default and a timeout, and alert on freshness, null rates and drift.

One stack, two contexts: home feed has no seed; up-next adds the current video. Candidates come from ANN over video embeddings, co-watch, subscribed communities and trending, then a GPU ranker reads the online feature store.

Logging is the backbone: every impression records request ID, model version, candidates, scores, position and features as served. Client events like watch seconds and skips join by request ID into skew-free training data.

Observability: per-stage latency SLOs, candidate counts per source, empty results, feature freshness, score and calibration drift, and per-surface business metrics. Ship via canary with automatic rollback.