Roblox·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Roblox ML engineer interview that went deep on recommendation systems. One long design question covering basically the entire ML lifecycle, from raw features to live A/B tests. Not a casual screen.

Questions Asked (1)

Design a game recommendation system for users. Cover the full ML pipeline: feature engineering, candidate generation, model selection, training data construction, loss functions, optimizers, offline metrics, and online experimentation.

System DesignA/B Testing & ExperimentationTechnical Trade-offs

Author's notes

This is a lot to cover in one question and I definitely didn't pace myself well.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and business objectives, then walk through the ML pipeline stages in a logical order, highlighting key design choices and trade-offs at each stage. Emphasize how offline metrics and online experimentation tie back to business goals, and discuss how you would iterate based on results.

Pro tip: At Roblox, focus on the unique aspects of a user-generated content platform: massive item catalog, implicit feedback from play sessions, and the need for real-time recommendations. Show awareness of cold-start and popularity bias, and propose solutions like content-based fallbacks and exploration strategies.

1. Clarify Requirements and Scope

Ask questions to understand the business goals, user base, and constraints. Define what a 'game' is (e.g., Roblox experiences), the recommendation context (homepage, after a session), and success metrics (e.g., engagement, retention).

2. Design the ML Pipeline Stages

Outline the end-to-end pipeline: data collection, feature engineering, candidate generation, ranking, and serving. For each stage, discuss key considerations such as feature types, model choices, and scalability.

3. Detail Model Training and Evaluation

Explain how to construct training data (e.g., implicit feedback from user interactions), choose loss functions (e.g., sampled softmax, BPR), optimizers (e.g., Adam), and offline metrics (e.g., recall@k, NDCG). Discuss how to handle negative sampling and biases.

4. Plan Online Experimentation and Iteration

Describe how to design A/B tests to measure online metrics (e.g., CTR, session length, retention). Discuss guardrail metrics, statistical power, and how to iterate based on results. Mention potential pitfalls like novelty effects and network effects.

5. Address Trade-offs and Scalability

Highlight trade-offs between model complexity and latency, batch vs. real-time features, and exploration vs. exploitation. Discuss how to scale the system to millions of users and items, and how to handle cold-start and long-tail content.

Key Points to Mention

Feature engineering: user features (demographics, historical interactions), item features (game metadata, embeddings), context features (time, device), and cross features.
Candidate generation: two-tower models, matrix factorization, or graph-based methods to retrieve top-N candidates from millions of items.
Model selection: ranking models like deep neural networks (e.g., DLRM) or gradient boosted trees, considering latency and interpretability.
Training data: implicit feedback (plays, likes), negative sampling strategies, and handling position bias.
Loss functions and optimizers: binary cross-entropy, pairwise losses (BPR), sampled softmax; optimizers like Adam or Adagrad.
Offline metrics: recall@k, precision@k, NDCG, MAP; online metrics: CTR, conversion rate, session time, retention; A/B testing with proper randomization and guardrails.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.