AI & Technical question
Scale sees that a coding or multimodal dataset is not producing the expected model gains. How would you define or refine the data specification, set up a quality review process, and determine whether the biggest issue is data coverage, annotation accuracy, task difficulty, or evaluation mismatch? What improvements would you prioritize first, and why?
- Scale AI
- AI & Technical
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests systematic root cause diagnosis for underperforming training data and the ability to prioritize fixes across coverage, accuracy, difficulty, and eval mismatch.
How to approach it
- Start by checking whether the evaluation used to judge model gains actually measures what the dataset was designed to teach, since eval mismatch is a common silent cause.
- Review data coverage against the target task distribution to see if key subcategories are underrepresented.
- Audit a sample of annotations for accuracy, since low quality labels can look like a coverage problem but are actually a QA process failure.
- Check task difficulty calibration, since data that is too easy or too hard for the current model capability level will not move performance.
- Refine the data specification based on the diagnosed cause, tightening annotation guidelines if it is accuracy, or expanding categories if it is coverage.
- Set up an ongoing quality review process, like inter annotator agreement tracking, to catch this earlier on future datasets.
What a strong answer includes
- Checks eval mismatch first, since it is the cause most likely to be overlooked and can invalidate the whole diagnosis if ignored.
- Separates coverage, accuracy, and difficulty as distinct hypotheses to test rather than treating dataset quality as one vague concept.
- Proposes an ongoing quality process, like inter annotator agreement, not just a one time fix, to prevent recurrence.
Common mistakes
- Assuming the dataset itself is flawed without first checking whether the evaluation is even measuring the right thing.
- Fixing annotation accuracy without checking whether coverage or difficulty calibration is the actual bottleneck.
Likely follow-up questions
- How would you measure inter annotator agreement and what threshold would concern you?
- What would you do if the eval mismatch turns out to be the real cause after the data has already shipped to the customer?
More ai & technical questions
- For a wealth-management copilot used by financial advisors, what metric stack would you put in place before and after launch to determine whether it is creating business value and whether it is safe enough for enterprise deployment? Be specific about leading vs. lagging metrics, model-quality/evaluation metrics, and launch guardrails.Scale AI · AI & Technical · Hard
- An enterprise customer wants a highly customized agent launched this quarter, but engineering believes the customer’s data quality is poor and the evaluation set is too weak to support a reliable release. How would you assess the risk, align on launch criteria, and handle the conversation with both the customer and the internal team if they disagree?Scale AI · AI & Technical · Hard
- You need to build training data and RL environments for agentic cybersecurity tasks without relying on hand-curated examples forever. How would you define the task taxonomy and the sourcing + QA pipeline so it scales while still controlling for contamination, reproducibility, and license/IP hygiene? Be specific about where you would automate versus require expert review.Scale AI · AI & Technical · Hard
- A frontier lab says existing security benchmarks are too shallow and too easy to game. Design an evaluation product where a task is marked solved only when the exploit reliably reproduces or the patch fixes the issue without breaking intended behavior. What would the task format, execution environment, grader design, and reward/verification logic look like?Scale AI · AI & Technical · Hard
- Tell me about a time you owned a platform or infrastructure capability rather than an app-layer feature. What was the problem, what core abstractions or architectural decisions did you make, how did you trade off speed versus production bar across areas like deployment, observability, or auth, and what did you learn from the outcome?Scale AI · AI & Technical · Hard
- For a core platform capability at Scale, how would you define 'done' differently at the platform layer versus the application layer? Use observability for AI agents as the example, and specify the production bar across instrumentation, debugging workflows, reliability, security/compliance, and adoption so that customers can trust it without thinking about it.Scale AI · AI & Technical · Hard
More questions from Scale AI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture