Mid-level interview questionAsked at Roblox · interview write-up
Design a classifier that assigns genres to games using text, images and tabular metadata, from label quality to serving and monitoring.
Model answer
Treat it as multi-label, since games often fit several genres. Clean labels first: creator tags are noisy, so audit a sample and flag likely errors with confident learning.
Encode text with a pretrained text encoder, thumbnails with a CLIP-style image encoder, and keep tabular metadata as features, then late-fuse them in a small classifier with per-genre thresholds. Score in batch on updates. Monitor per-genre precision on audited samples, and drift as new genres appear.
Practise the rest
12 Multimodal questions, 492 in all, each with a model answer. Free.
More from Roblox interview questions.
More Multimodal interview questions
- What has driven each generation of LLM improvements, how do multimodal LLMs work, and what role does normalisation play in LLMs?
- You need to deploy multimodal models under tight GPU and latency limits, and also support fast retrieval over video. How do you approach it?
- How does a vision encoder feed images into a multimodal LLM, and where are the bottlenecks?
- Design the training of a multimodal embedding model: architecture, supervision and losses, evaluation metrics and deployment.
- Turn a multimodal model that captions videos into a production service under tight compute and GPU memory limits.
- For an offline audio detection system, outline the ML inference and labeling pipeline: feature extraction, keyword spotting, thresholding, calibration and human labeling.