Meta Generative AI Engineer Interview Questions (2026)

1. 1Recruiter screen30 minEasy

   Process overview, level calibration, and prep guidance — Meta recruiters actively coach on round formats.

2. 2Technical screen45 minHard

   Two DSA problems in 45 minutes on a plain shared editor with no autocomplete or execution.

3. 3Coding round ('Ninja')45 minHard

   Two more problems at loop difficulty; clean near-compilable code and verbalized complexity analysis expected.

4. 4System design ('Pirate')45 minHard

   Design a Meta-scale product system (feed, Stories, chat) with emphasis on read-heavy fan-out, caching, and data modeling.

5. 5Behavioral ('Jedi')45 minMedium

   Deep past-experience discussion on conflict, growth, and impact aligned to Meta values; graded as a real signal round.

1. Q1

   At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on an AI assistant for creators. Describe the observe-plan-act loop for an AI agent and where failures occur

   FoundationalAgentic product designAgents

   How to answer: The agent observes state, plans next steps, calls tools/actions, updates state, and repeats until done. Failures include wrong goals, bad plans, tool errors, stale state, infinite loops, and unsafe actions. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

2. Q2

   At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a messaging assistant with high daily traffic. When should you use a deterministic workflow instead of an autonomous agent?

   FoundationalAgentic product designAgents

   How to answer: Use workflows when steps are known, compliance matters, and determinism is valuable. Use agentic behavior for ambiguous tasks requiring planning, but bound it with state machines, budgets, and approvals. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

3. Q3

   At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a content integrity workflow. How should an agent break a large task into subtasks?

   FoundationalIntegrity, evals, and measurementAgents

   How to answer: Use explicit goals, constraints, intermediate artifacts, dependency ordering, and stop criteria. Persist the plan and verify each subtask before moving to irreversible actions. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

4. Q4

   At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a retrieval feature for help-center answers. Design memory for a personal assistant agent without creating privacy risks

   FoundationalCoding screenAgents

   How to answer: Separate short-term session state from long-term user preferences, require user-visible controls, store minimal facts with provenance, expire stale data, and avoid storing sensitive information by default. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

5. Q5

   At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a GenAI tool that must be efficient on mobile. An agent has ten tools and often calls the wrong one. How do you improve it?

   IntermediateBehavioral / executionAgents

   How to answer: Improve tool descriptions and schemas, add examples, reduce overlapping tools, add a router or policy layer, validate arguments, and evaluate tool selection on labeled tasks. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

6. Q6

   At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on an AI assistant for creators. Which actions should require human approval in an enterprise agent?

   IntermediateAgentic product designAgents

   How to answer: Irreversible, external, high-cost, sensitive-data, permission-changing, or policy-impacting actions should require approval. The approval UI should show planned action, inputs, risks, and alternatives. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

7. Q7

   At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a messaging assistant with high daily traffic. How would you design an agent that runs for hours across multiple tools?

   IntermediateAgentic product designAgents

   How to answer: Use durable state, checkpoints, idempotent tool calls, resumable queues, timeouts, progress reporting, cancellation, and audit logs. Treat it like a distributed workflow. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

8. Q8

   At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a content integrity workflow. How do you keep an agent from spending too much money or time?

   IntermediateIntegrity, evals, and measurementAgents

   How to answer: Set budgets for tokens, tool calls, wall-clock time, retries, and external API costs. The agent should summarize progress and ask for permission or degrade gracefully when near budget. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

9. Q9

   At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a retrieval feature for help-center answers. What state should be persisted between agent steps?

   IntermediateCoding screenAgents

   How to answer: Persist user goal, plan, tool calls/results, decisions, intermediate artifacts, permissions, budgets, and trace IDs. Avoid persisting raw sensitive data unless necessary and approved. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

10. Q10

    At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a GenAI tool that must be efficient on mobile. An agent retries a failing tool and creates duplicate tickets. What went wrong?

    IntermediateBehavioral / executionAgents

    How to answer: The tool call was not idempotent or lacked a deduplication key. Add idempotency tokens, retry policies by error class, confirmation for side effects, and reconciliation checks. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

11. Q11

    At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on an AI assistant for creators. How would you sandbox an agent that can run code?

    SeniorAgentic product designAgents

    How to answer: Run code in isolated containers with resource limits, network restrictions, file-system boundaries, secret isolation, malware checks, and logs. Require approval for external writes or privileged operations. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

12. Q12

    At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a messaging assistant with high daily traffic. When are multiple agents helpful, and when are they overkill?

    SeniorAgentic product designAgents

    How to answer: They help when roles require different tools or independent checks, but add coordination cost and nondeterminism. Prefer single-agent or workflow designs unless role separation measurably improves outcomes. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

13. Q13

    At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a content integrity workflow. Should an agent critique its own work before finalizing?

    SeniorIntegrity, evals, and measurementAgents

    How to answer: Self-critique can catch simple errors but is not a guarantee. Use it as one layer alongside deterministic validators, external tools, test cases, and human review for high-risk tasks. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

14. Q14

    At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a retrieval feature for help-center answers. What dashboard would you build for an agentic feature?

    SeniorCoding screenAgents

    How to answer: Show task success, abandonment, tool-call count, cost, latency, retry rate, approval rate, failure categories, safety events, and per-tool error rates. Include trace drill-down. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

15. Q15

    At Meta, you are interviewing for the Integrity/Evals/Agents Engineer loop and working on a GenAI tool that must be efficient on mobile. How should an agent handle a user changing their mind mid-task?

    SeniorBehavioral / executionAgents

    How to answer: Support cancellation and replanning, mark in-flight side effects, confirm irreversible actions, update state, and explain what has already happened. Use checkpoints to avoid inconsistency. In this Meta loop, I would explicitly connect the decision to pragmatic execution, product scale, ranking/relevance, measurable impact, efficiency, and integrity/safety trade-offs.

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Be fast and correct on coding; for design, drive the conversation; prepare impact-focused behavioral stories

Indicative Generative AI Engineer pay in India: ~₹14–60 LPA (role-level range, not a Meta-specific figure).

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