Product design question
How would you improve model discovery on the Hugging Face Hub with 2.4M+ models?
- Hugging Face
- Product design
- Hard
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What this question tests
Product design skills applied to search and discovery at extreme scale, where most of the catalog is low quality or duplicative.
How to approach it
- Clarify the user segments: a researcher hunting for a specific architecture, a builder wanting the best model for a narrow task, and a beginner who does not know what to search for.
- State the core pain point: 2.4 million models means keyword search surfaces noise, and quality signals like downloads favor old popular models over better new ones.
- Prioritize the builder-with-a-task segment first, since it is the largest source of Hub value and easiest to serve with structured filters.
- Propose the solution: task-based filtering, benchmark-backed leaderboards per task, and a verified quality signal like maintainer reputation or eval scores shown inline.
- Define the success metric: increase in models tried per session or reduction in time from search to first inference call.
What a strong answer includes
- Separates discovery intent explicitly, since a researcher and a task-driven builder need different UI, not one unified search box.
- Proposes surfacing eval-based leaderboards per task category rather than relying purely on download counts, which favor incumbency.
- Suggests a recommendation layer based on the user's own past downloads or Space usage to personalize discovery.
- Assumes a candidate metric, like reducing median time-to-first-successful-inference call by 30 percent, marked clearly as an assumption.
Common mistakes
- Proposing a generic search redesign without addressing the specific scale and duplication problem in the model catalog.
- Ignoring that download count as a quality proxy actively hurts newer, better models.
Likely follow-up questions
- How would you handle near-duplicate model uploads, like ten fine-tunes of the same base model?
- How would you personalize discovery for a user with no download history?
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More questions from Hugging Face
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop