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Meta Behavioral Interview Template — AI Engineer (Menlo Park)

This behavioral interview evaluates how an AI Engineer operates within Meta’s culture and execution model. Expect a fast-paced, drill‑down conversation focused on end‑to‑end ownership, data‑driven decisions, and measurable impact in production AI/ML. Format (approx. 45 minutes): 3–5 min introductions and role context; 30–35 min of two deep‑dives on your highest‑impact AI/ML projects; 5–7 min candidate questions. Interviewers probe for Meta values in action: moving fast without breaking things that matter, focusing on impact, acting as an owner, being direct and respectful, and building awesome things. Specific focus areas and probes (AI‑specific): - End‑to‑end AI delivery: A time you shipped an ML system from problem framing → data strategy/labeling → modeling → evaluation → rollout/guardrails → post‑launch iteration. Expect timeline reconstruction, your individual contributions, and trade‑offs you made (e.g., model quality vs. latency, infra cost vs. recall). - Metrics and experimentation: Defining North‑Star and guardrail metrics (e.g., precision/recall/latency/coverage, integrity metrics), designing A/Bs or interleavings, handling metric conflicts, reading noisy experiment results, and deciding when to ship, hold back, or roll back. - Ambiguity and product sense: How you clarified ambiguous problem statements, sized opportunities, aligned with PM/DS/Design/Infra, and translated product goals into ML objectives; how you balanced research novelty vs. pragmatic impact. - Safety, integrity, and privacy: Building with responsibility—abuse/misuse considerations, bias/fairness checks, privacy‑aware data use, model red‑teaming, rate‑limiting and policy alignment; how you handled pushback when safety constraints affected velocity. - Reliability and on‑call: Handling SEVs related to models (data drift, feature pipeline breaks, model regressions), incident coordination, canaries/shadow traffic, feature flags, and your postmortem actions to prevent repeats. - Collaboration and feedback: Examples of direct, respectful feedback, unblocking others, influencing across teams/orgs, and navigating disagreement with senior stakeholders under time pressure. Evidence the interviewer expects: quantified results (e.g., +X% precision at Y ms p95; −Z% inference cost; +A% integrity metric; launch to N countries), clear articulation of alternatives considered and why they were rejected, and concrete learnings you reused elsewhere. Common question stems: “Tell me about the highest‑impact ML system you’ve shipped and your exact role.” “Describe a time you moved fast on an AI project and a risk you consciously accepted—how did you mitigate it?” “Walk me through an experiment that failed and what you changed.” “How did you ensure fairness/privacy in a model that affected user experiences?” “Describe a conflict with a PM or Infra partner over a launch criterion—how did you resolve it?” “Tell me about a production incident tied to model drift—what were the leading indicators and long‑term fixes?” Evaluation signals (what strong looks like at Meta): crisp problem framing; owner mindset; bias to action with safety guardrails; rigorous metrics and experimentation; transparency about trade‑offs and mistakes; influence without authority; and iterative learning captured in docs/playbooks.

engineering

45 minutes

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About This Interview

Interview Type

BEHAVIOURAL

Difficulty Level

4/5

Interview Tips

• Research the company thoroughly

• Practice common questions

• Prepare your STAR method responses

• Dress appropriately for the role