[D] Diffusion research interview experience?

A detailed Reddit post is going viral within the AI research community, offering a rare, crowdsourced look into the rigorous interview process for coveted diffusion model roles. User total_expectation solicited firsthand accounts from candidates who have interviewed for Research Scientist (RS) and Research Engineer (RE) positions specializing in diffusion—the technology behind image generators like Stable Diffusion, DALL-E, and Midjourney. The post seeks to demystify the preparation required, asking for specifics on key papers, courses, and the exact nature of technical grilling.

Respondents and discussion reveal that interviews are intensely technical and role-specific. For Research Scientist positions, candidates report facing proof-heavy questions, deriving loss functions from scratch, and deep discussions on open theoretical problems in diffusion. For Research Engineer roles, the focus shifts to practical implementation details, scaling challenges for training, robust evaluation methodologies, and adapting models to new modalities like video or 3D. Both roles reportedly require critiquing recent papers (e.g., on rectified flow or consistency models) and brainstorming novel research directions on the spot, beyond standard Leetcode problems.

The thread underscores the specialized expertise demanded by leading AI labs (e.g., OpenAI, Google DeepMind, Stability AI) pushing the boundaries of generative AI. Success requires moving far beyond textbook knowledge to engage with cutting-edge literature, demonstrate hands-on implementation skill, and articulate a clear, critical research mindset. This transparent sharing of interview tactics is becoming an invaluable resource for aspirants in a hyper-competitive field.