Roblox ML Interview: Feature Engineering & Encoding

Core task

You will be asked to design and justify feature engineering strategies for real-world datasets common in recommendations and user-facing ML at Roblox. Expect questions about handling categorical variables (especially high-cardinality and long-tail distributions), converting user interaction logs into usable features, and choosing/regularizing supervised encodings like target encoding.

Flow / stages

You’ll typically walk the interviewer through: (1) Data profiling — describe cardinality, sparsity, missingness and time dependencies; (2) Candidate encodings — one-hot, frequency, hashing, embeddings, and target/smoothed target encoding; (3) Implementation details — cross-validation to avoid leakage, smoothing/hyperparameters, memory and latency trade-offs for online inference; (4) Evaluation — A/B, offline metrics, and robustness checks (cold-start, distribution shift).

Skills & signals

You should demonstrate knowledge of: how and when to use target encoding (leave-one-out, K-fold, smoothing), techniques to mitigate leakage, handling long-tail categories (rare grouping, frequency buckets, hashing), building aggregated user features from interaction sequences, and practical concerns like model input size, inference latency, and monitoring feature drift.

Practical tips

Explain concrete preprocessing steps, show awareness of time-based splitting to prevent label leakage, and describe fallback strategies for cold-start or streaming data. Mention trade-offs between model expressivity (embeddings) and production constraints (memory, latency).