Meta Behavioral & Ownership interview questions

Explain a reliability concern with evidence, a specific proposal and an honest outcome. Show how you weighed the cost of delaying a launch and what changed afterward.

Tell an incident through the decisions you made, the evidence available at the time and the cost of recovery. Keep your role clear and distinguish mitigation from finding the root cause.

Clarify what the 256-GPU request must accomplish, then compare it with real placement and quota constraints. Offer a feasible alternative and explain who can decide when teams’ priorities conflict.

Explain the reason for an unpopular migration, choose informative rollout cohorts and describe recovery, communication and transition cost without inventing an incident.

Explain how a plausible diagnosis became too certain, what disproved it and what changed in your investigation. Separate observed symptoms from causal evidence.

Plan a useful first month by tracing a real job, reviewing incidents, helping under supervision and shipping a bounded improvement with the team.

Separate a disagreement about measurements from a real capacity shortage. Work a reclaim example, expose the missing reserve and escalate an authorized decision.

Explain what grew, which constraints appeared and how the system stayed usable. Work through metric-cardinality and device-event examples without assuming a universal failure order.

Evaluate a proposed 2,000-GPU purchase using compatible capacity, dated demand and complete alternatives. Make any gap left by reclaim visible.

Support a new responder without turning an outage into an exam. Choose an appropriate role, explain handoffs and review the decisions afterward.

Defend a specific infrastructure opinion with scoped evidence, useful arithmetic and a condition that would change your view.

Explain a decision under uncertainty through the cost of waiting, bounded exposure and evidence that changes the next action.

Turn a design disagreement into a concrete comparison. Use a gang-scheduling example to explain evidence, decision ownership and responsible follow-through.

Describe senior and staff responsibility through scope, technical judgment, influence and durable results, while recognizing that company rubrics differ.

Connect a researcher tool to the workflow it improved. Measure the whole loop, meaningful adoption and effects on other users.

Offer a first-90-days plan with useful early work and clear decision points. Let the team’s evidence and constraints determine the deliverable.

Report a capacity shortfall with assumptions, consequences, feasible options and a decision deadline. Show how one uncertain project changes the forecast.

Build a five-year infrastructure outlook from explicit assumptions, competing explanations and evidence that could change your view. Connect each prediction to a practical engineering decision.

Compare net time saved, maintenance and failure risk before automating. Work a payback example and explain why concurrency limits alone do not bound repeated damage.

Connect an infrastructure problem you want to solve with work you have actually done. Explain what prompted the move now, what transfers from your background and what you still need to learn.

Connect your view on AI safety to systems you could build: model access, audit evidence, evaluation capacity and a tested way to stop or reverse a release. Explain the costs and limits of each control.

Connect a performance change to its measured mechanism. Compare fair baselines and calculate how a local speedup affects the whole workload.

Handle a request to skip a safety check by identifying the required gate, finding approved alternatives and making exception authority explicit.

Build a learning habit around questions, source evidence and practical tests. Explain a real example without claiming work you did not perform.