Technical practice
How would you design an agent to investigate a suspected bug across code, traces, logs, and runtime behavior?
What to coverThe role calls for agents that use these sources to find and explain bugs. Suggested answer: describe how the agent selects evidence, tests competing explanations, records its reasoning, and handles inconclusive results.
How would you give an investigative agent access to sandboxes, browsers, and web research?
What to coverThe posting explicitly includes these tools in agent investigations. Suggested answer: discuss when each tool is useful, how to limit its actions, and how to distinguish observed evidence from a model's inference.
How would you structure an LLM pipeline from triage through root-cause analysis to a proposed fix?
What to coverThese three stages are named responsibilities. Suggested answer: explain the inputs and outputs of each stage, how uncertainty passes between them, and what evidence would justify a fix proposal.
How would you route investigation tasks among models while controlling cost?
What to coverThe role includes sending each job to a suitable model at an appropriate cost. Suggested answer: compare routing by task difficulty and evidence needs, then explain how you would evaluate quality against cost rather than optimizing either alone.
How would you detect meaningful product-experience anomalies in noisy data?
What to coverThe posting calls for anomaly detection across product behavior, performance, and user experience. Suggested answer: define a useful signal and baseline, then address false alerts, missed failures, and changes in normal behavior.
How would you build an evaluation dataset for bug-finding and explanation agents?
What to coverInterfere lists evaluations and datasets as core work. Suggested answer: cover case selection, evidence-backed labels, measures for detection and explanation quality, and ways to avoid judging a system only on familiar cases.
What would you observe in production to tell whether an agentic system is improving?
What to coverThe role calls for observability and measurable feedback loops for systems used by real teams. Suggested answer: distinguish operational measures from outcome quality, and explain how you would investigate a metric change before attributing it to a model improvement.
How would you construct useful codebase context for a model?
What to coverThe posting names retrieval, indexing, context construction, and customer-stack integrations. Suggested answer: discuss how to find relevant code, connect it to runtime evidence, manage context limits, and check whether retrieval helped the investigation.
How would you turn bugs found and fixed into better future detection?
What to coverThe role explicitly calls for learning from resolved bugs. Suggested answer: describe how to capture trustworthy feedback, update detection or evaluations, and guard against treating an incorrect fix or incomplete diagnosis as ground truth.
What changes when an ML or LLM investigation system moves from prototype to production?
What to coverThe posting emphasizes systems real users depend on, rather than notebooks or demos. Suggested answer: address reliability, failure handling, measurement, and the trade-off between investigative depth and timely results.
Behavioural practice
Tell me about a time you took an ambiguous technical problem from idea to a useful result.
What to coverThe role calls for independently owning 0-to-1 AI work. Suggested answer: use a real situation, the scope and next step you chose, your actions, and the result; be clear about what remained uncertain.
Tell me about a time an evaluation changed your technical direction.
What to coverThe posting treats evaluations as a first-class engineering problem. Suggested answer: describe the initial approach, the evidence that challenged it, what you changed, and the resulting outcome.
Tell me about a time you learned an unfamiliar technology or domain quickly enough to deliver work.
What to coverThe posting values moving between research and engineering and learning unfamiliar technology quickly. Suggested answer: set out the situation, how you narrowed what to learn and tested it, and what your work achieved.
Tell me about a time you balanced shipping speed with maintainable engineering.
What to coverInterfere says it ships daily and deliberately without letting technical debt accumulate. Suggested answer: explain the time pressure, the trade-off you made, the actions that protected quality, and the result.
Tell me about a time you helped people with different roles reach a shared understanding of a problem.
What to coverThe product aims to give product, design, and engineering teams context for the same user-experience failure, and the posting emphasizes in-person architecture discussions. Suggested answer: describe the situation, how you presented evidence and handled differing priorities, and what the group decided or changed.