AI & Technical question
A frontier lab says its model scores well on public benchmarks but still fails on long-horizon, repo-scale engineering tasks. How would you design a contamination-resistant evaluation or RL environment that surfaces those failures, while preserving reproducibility, trustworthy reward signals, and automated code-correctness verification?
- 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
The answer guide for this question is on its way. You can still practice it now.
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