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About this interview
A technical interview on Generative AI & Multimodal Models, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain denoising diffusion models at a conceptual level: forward noising, reverse denoising, score matching
Compare autoregressive and diffusion generation paradigms across modalities including speed and quality tradeoffs
Describe flow matching as a faster alternative to DDPM-style diffusion
Discuss VAEs and VQ-VAE as image tokenizers enabling autoregressive image generation
Topics covered
Generative model foundationsAutoregressive generationVAEsVQ-VAEVQ-VAE as image tokenizerDiffusion — forward processDiffusion — reverse processScore matchingAR vs Diffusion comparisonDiffusion — fast samplersLatent diffusionClassifier-free guidanceDiT architectureFlow matching
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“At a high level, what is the goal of a generative model, and how does it differ from a discriminative model?
“What is classifier-free guidance in diffusion models? How does it allow you to trade off sample quality against diversity?
“How do you evaluate the quality and diversity of samples from a generative image model? Describe FID, Inception Score, and their limitations.