PhD-level research-to-production interview for large-scale recommendation, ranking, and retrieval systems powering Roblox discovery

This is a PhD early-career ML role on Roblox's Search and Discovery or Safety/Alt Defense teams, building recommendation, ranking, and retrieval systems at massive scale. The interview centers on your research depth (publications in venues like SIGIR, KDD, RecSys, ICLR, ICML, NeurIPS), your ability to translate research into production ML systems for hundreds of millions of daily users, and hands-on modeling skill in areas like personalization, attention mechanisms, and generative/multimodal models.

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What this interview tests

Recommender systems, ranking, and retrieval at massive scale
Research depth: thesis alignment and publication record in top ML/IR venues
Generative and multimodal models (LLMs, VLMs, VLAs) applied to recommendation
Personalization and attention mechanisms (sparse/linear attention)
Translating research into production ML systems
Programming proficiency (Python, C++, Go, or Java)

Common question themes

Walk me through your PhD thesis and how it connects to recommendation/search/generative modeling

Design a ranking or retrieval system for a Roblox discovery surface (e.g., experiences or avatars)

How would you use attention mechanisms for user-interest modeling at scale

How would you take a research idea from your publications into a production ML pipeline

Explain a tradeoff you made between model complexity and serving latency/scale

How would you detect or model recidivist bad actors across billions of accounts (if Safety track)

How candidates describe it

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