Google AI Engineer Interview Questions (2026)

1. 1Recruiter screen30 minEasy

   Background, level calibration, and process walkthrough with a recruiter.

2. 2Technical phone screen45 minHard

   One or two DSA problems solved live in a shared editor with emphasis on optimal complexity and clean code.

3. 3Coding round (onsite)45 minHard

   Harder DSA with follow-up constraint changes; interviewer scores GCA and RRK on a rubric.

4. 4System design round45 minHard

   Design a planet-scale system (e.g. a piece of Search or YouTube) with explicit capacity estimates and tradeoffs.

5. 5Googleyness & Leadership45 minMedium

   Behavioral round on collaboration, ambiguity, and user-first judgment scored against Google's structured rubric.

6. 6Hiring Committee review30 minMedium

   No candidate interaction; the written feedback packet is reviewed and the hire/no-hire decision is made, followed by team matching.

1. Q1

   At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a Gemini feature for Workspace users. Describe the observe-plan-act loop for an AI agent and where failures occur

   FoundationalAgentic systems 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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

2. Q2

   At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a Vertex AI enterprise application. When should you use a deterministic workflow instead of an autonomous agent?

   FoundationalAgentic systems 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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

3. Q3

   At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a large-scale search-grounded assistant. How should an agent break a large task into subtasks?

   FoundationalEvaluation and launch readinessAgents

   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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

4. Q4

   At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on an AI coding workflow integrated with developer tools. Design memory for a personal assistant agent without creating privacy risks

   FoundationalCoding interviewAgents

   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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

5. Q5

   At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a multilingual AI product used globally. An agent has ten tools and often calls the wrong one. How do you improve it?

   IntermediateGoogleyness / leadershipAgents

   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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

6. Q6

   At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a Gemini feature for Workspace users. Which actions should require human approval in an enterprise agent?

   IntermediateAgentic systems 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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

7. Q7

   At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a Vertex AI enterprise application. How would you design an agent that runs for hours across multiple tools?

   IntermediateAgentic systems 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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

8. Q8

   At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a large-scale search-grounded assistant. How do you keep an agent from spending too much money or time?

   IntermediateEvaluation and launch readinessAgents

   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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

9. Q9

   At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on an AI coding workflow integrated with developer tools. What state should be persisted between agent steps?

   IntermediateCoding interviewAgents

   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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

10. Q10

    At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a multilingual AI product used globally. An agent retries a failing tool and creates duplicate tickets. What went wrong?

    IntermediateGoogleyness / leadershipAgents

    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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

11. Q11

    At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a Gemini feature for Workspace users. How would you sandbox an agent that can run code?

    SeniorAgentic systems 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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

12. Q12

    At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a Vertex AI enterprise application. When are multiple agents helpful, and when are they overkill?

    SeniorAgentic systems 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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

13. Q13

    At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a large-scale search-grounded assistant. Should an agent critique its own work before finalizing?

    SeniorEvaluation and launch readinessAgents

    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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

14. Q14

    At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on an AI coding workflow integrated with developer tools. What dashboard would you build for an agentic feature?

    SeniorCoding interviewAgents

    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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

15. Q15

    At Google, you are interviewing for the Agents & Evaluation Engineer loop and working on a multilingual AI product used globally. How should an agent handle a user changing their mind mid-task?

    SeniorGoogleyness / leadershipAgents

    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 Google loop, I would explicitly connect the decision to scale, rigorous problem solving, product impact, reliability, data-driven decisions, and clear trade-off analysis.

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Master DSA and communicate your thinking out loud; use Google's structured Explain-Clarify-Improve approach; prepare for Googleyness/behavioral

Indicative AI Engineer pay in India: ~₹12–55 LPA (role-level range, not a Google-specific figure).

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