Did the Roblox MLE loop a few months back. Got the offer, didn't take it (team fit wasn't right for me), but the process was interesting enough to write up.
Roblox ML is mostly recsys and content moderation. Their main ML surface is the discovery feed (games/experiences), ad ranking, and safety/trust systems. If you come in expecting to talk about LLMs you might be surprised. It's very much classical recommendation systems and classification.
The rounds:
Coding: One LC-style round, medium difficulty. Nothing ML-specific in the coding round, just general DS+A. I got a graph problem (shortest path variant). Pretty standard.
ML design: This is the real interview. I was asked to design a recommendation system for the Roblox homepage. It needed to handle cold start (new users with no history), balance engagement vs. creator diversity, and work at scale (they have hundreds of millions of MAU). They care a lot about how you think about the objective function, what proxy metrics you'd optimize for in stage 1, and what you'd watch as your leading indicator of success. Make sure you can talk about embedding models, two-tower architectures, and approximate nearest neighbor search. Also: online vs. offline evaluation, A/B testing design.
ML breadth: One round that was more conversational. They asked about my experience with different model types, feature engineering for sparse data, and a case study from my past work. 'Tell me about a model you shipped and what happened after you shipped it' came up. They want to hear about monitoring, drift, failure modes.
Behavioral: Pretty normal. One specific question was 'tell me about a time you had to push back on a product decision because the ML wasn't ready.' That's a real Roblox thing, apparently. Their product teams want fast iteration and ML needs more runway, so that tension is real.
Comp for my offer (senior MLE level, San Mateo): Base was $195k, RSUs over 4 years. Total comp year 1 was in the $280-300k range with the equity tranche. Reasonable for the Bay Area but behind Google/Meta for the same level.
If you're coming from ads-recsys or content safety ML, this is a natural fit. If your background is purely CV or LLM finetuning, expect to do some translation work in the design round.