Build facial age estimation and deepfake defenses for Roblox's billion-user safety platform

Roblox is hiring a PhD-track early-career ML engineer for its Account Identity team to build in-house Facial Age Estimation and centralized age-assurance controls, defending against deepfakes and identity spoofing using VLMs and multimodal learning. This is a research-to-production role: expect deep technical questions on computer vision/adversarial ML plus large-scale data engineering (Spark/SQL).

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What this interview tests

Computer vision / multimodal learning / VLMs for facial representation and age estimation
Deepfake detection and adversarial machine learning
Productionizing end-to-end ML lifecycles: data engineering to scoring
Large-scale behavioral data analysis (Spark, SQL)
Precision/recall tradeoffs in adversarial, safety-critical systems
Research-to-production translation from PhD thesis work

Common question themes

Walk through your thesis or research project most relevant to facial representation, deepfake detection, or VLMs

How would you design a facial age estimation system, including how you'd evaluate precision at scale

Describe how you'd extract meaningful behavioral signal from large, noisy account-level logs

How do you think about an adversary trying to spoof or evade your detection model

Walk through productionizing an ML model end-to-end: data pipeline, training, scoring, monitoring

How do you balance catching bad actors against not degrading the experience of legitimate users

How candidates describe it

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