LIKELY QUESTIONS

- Describe a computer-use agent or multimodal system you have built, trained, or evaluated. What was the task, architecture, and measurable outcome?
- How would you design an evaluation suite for an agent that must complete long-horizon tasks across real computer interfaces?
- A computer-use policy succeeds on short tasks but fails late in long workflows. How would you diagnose whether the problem is perception, planning, action grounding, memory, or environment design?
- Tell me how you would build an RL environment for browser or desktop interaction that is realistic, reproducible, safe, and difficult to exploit.
- What approaches would you consider for sparse-reward, long-horizon reinforcement learning in computer-use tasks, and what tradeoffs would you expect?
- How would you evaluate whether a model genuinely completed a task rather than exploiting a bug in the evaluator or environment?
- Describe a time you moved an ML or research capability from an experiment into a reliable production system. What engineering changes were required?
- How do you think about safety, oversight, and misuse prevention for an agent that can operate software and take consequential actions?

BEHAVIOURAL QUESTIONS

- Tell me about a time an experiment produced surprising or negative results. How did you respond?
Model approach: Situation: Briefly define the hypothesis, system, and expected result. Task: State the decision or research question you owned. Action: Explain how you validated the result, ruled out implementation or data issues, formed revised hypotheses, and ran targeted follow-up experiments. Result: Quantify what you learned, the resulting change in direction, and how the negative result improved the team's future work.

- Describe a time you worked across research, infrastructure, and product teams to deliver a complex ML capability.
Model approach: Situation: Set up the differing goals, such as research quality, training reliability, latency, cost, or product constraints. Task: Clarify your ownership and success criteria. Action: Describe how you established shared metrics, interfaces, milestones, experiment tracking, and decision-making cadence. Result: State the shipped outcome, measurable impact, and how collaboration reduced iteration time or operational risk.

- Tell me about a time you identified a reliability or safety risk in an AI system before it caused harm.
Model approach: Situation: Explain the system and the potentially harmful failure mode. Task: State your responsibility for assessing and mitigating the risk. Action: Describe the evaluation, red-team scenario, root-cause analysis, guardrails, and monitoring you implemented. Result: Quantify the reduction in failures where possible, explain what was changed before launch, and identify the lasting process improvement.

- Give an example of when you had to make progress amid ambiguity and incomplete data.
Model approach: Situation: Describe the ambiguous problem, competing hypotheses, and constraints. Task: Define the concrete decision, prototype, or research milestone you needed to deliver. Action: Explain how you decomposed uncertainty, selected high-information experiments, documented assumptions, and communicated tradeoffs. Result: Highlight the decision reached, evidence generated, and how the work created a clearer path for the team.

SMART QUESTIONS TO ASK

- What are the most important capability and reliability bottlenecks for Claude's computer use today: perception, action grounding, planning, memory, environment coverage, or something else?
- How does the team define and measure success for long-horizon computer-use tasks, particularly when task completion, safety, and robustness can conflict?
- What is the current workflow from a promising research result to inclusion in training or product systems, and where does that process most often break down?
- How are RL environments and evaluations designed to minimize reward hacking, evaluator leakage, and overfitting to benchmark-specific artifacts?
- What would distinguish exceptional performance in this role after six and twelve months?

RED FLAGS TO WATCH FOR

- Vague answers about safety boundaries, human oversight, deployment gating, or how the team handles agents taking irreversible actions in real software.
- An evaluation culture focused primarily on headline benchmark scores without clear discussion of distribution shift, reward hacking, reproducibility, and failure analysis.
- Persistent ambiguity about ownership between research, infrastructure, and product teams, especially if no one can explain how research prototypes become maintained production capabilities.