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Big Tech · International · Loop reviewed 2026-09-26

Most loops are two to four conversations of up to an hour. Technical rounds ask you to code in your strongest language in a tool that runs the code, and to test before you say done.

On this page: Interview loop · Official preparation material · Questions (15)

Roles covered

• AI Engineering: Software engineer (general interview baseline)
• AI Leadership: AI product manager (senior to group)

Interview loop

Stage	What happens	Basis
Screening and interviews	Some roles start with a short screening conversation. Most interviews are two to four conversations of up to an hour with potential teammates and cross-functional colleagues; some roles ask for code samples, a portfolio or other work samples.	✅ confirmed by the company How we hire, Microsoft Careers, retrieved 2026-09-26
Technical interviews	About 45-minute rounds on problem solving, design, clean code and testing, in a third-party tool where code compiles and runs; code in the language you know best, not pseudocode.	✅ confirmed by the company Technical interviewing, Microsoft Careers, retrieved 2026-09-26
AI product manager path	Recruiter and hiring manager screens of about 30 minutes each, then three to six hour-long rounds: behavioral, AI product design, technical and AI fluency, strategy, execution and prioritisation, and cross-functional collaboration.	† prep guide or compilation Microsoft AI Product Manager Interview Guide, Aced (formerly Exponent), retrieved 2026-09-26

Coding: varies by role. Required in technical interviews (Technical interviewing, Microsoft Careers, retrieved 2026-09-26)

Official preparation material

• How we hire, Microsoft Careers, retrieved 2026-09-26
• Technical interviewing, Microsoft Careers, retrieved 2026-09-26

Questions reported for Microsoft

Inference, serving and cost

• Distinguish time to first token, time per output token, inter-token latency and throughput when comparing serving systems.
  • Knowledge · Asked at: Microsoft †, Perplexity † · ✍ Answer
  • Tests: Whether user-visible delay is separated from aggregate capacity.
  • A strong answer covers:
    • Define TTFT as queueing plus prefill, TPOT and inter-token latency as decode pace, throughput as aggregate capacity.
    • Explain how larger batches trade each user's decode speed for throughput, and how admitted prefills stall decodes.
    • Compare at matched load and length distributions: p50 and p99 per latency metric, throughput within the targets.
  • Read: vLLM documentation (vLLM project)
• You are asked to reduce serving cost by an order of magnitude. Rank the levers and explain how you would test whether that target is achievable.
  • Applied scenario · Asked at: Amazon †, Microsoft † · ✍ Answer
  • Tests: Whether savings are measured under explicit quality and latency constraints.
  • A strong answer covers:
    • Measure cost per successful task at the required quality and SLO.
    • Compare smaller models, shorter contexts, reuse, batching and precision changes.
    • Benchmark each change and check whether the combined savings actually reach the target.
  • Read: Optimizing inference (Hugging Face Transformers docs) · Site Reliability Engineering (Google)

RAG and retrieval

• When would you combine lexical and dense retrieval, and how would you merge their rankings?
  • Knowledge · Asked at: Microsoft †, Perplexity † · ✍ Answer
  • Tests: Whether exact terms and semantic similarity receive appropriate treatment.
  • A strong answer covers:
    • Explain lexical strength on identifiers and dense strength on paraphrases.
    • Fuse rankings without assuming raw scores are comparable.
    • Ablate each retriever and reranking on a fixed relevance set.
  • Read: Contextual Retrieval in AI Systems (Anthropic)
• Where should a cross-encoder reranker sit in a retrieval pipeline, and when does its quality gain justify latency?
  • Knowledge · Asked at: Microsoft †, Perplexity † · ✍ Answer
  • Tests: Whether candidate count, recall and reranking cost are balanced.
  • A strong answer covers:
    • Retrieve a broad candidate set first, then score query-document pairs with the reranker.
    • Measure initial recall separately: a reranker cannot recover a document missing from its input.
    • Sweep candidate and final-context counts against held-out answer quality and end-to-end latency.
  • Read: Contextual Retrieval in AI Systems (Anthropic)
• Design retrieval that enforces the source system's access rights, including permission changes and shared caches.
  • System design · Asked at: Databricks †, Microsoft †, Palantir † · ✍ Answer
  • Tests: Whether access checks cover the full path to generated output.
  • A strong answer covers:
    • Propagate identity and enforce authorisation before content reaches the model.
    • Keep permission updates, deletions and cache keys consistent with the policy.
    • Test denied access and revocation paths; citations and logs need the same boundary.
  • Read: Document-level access control (Microsoft Learn)

Agents, tools and protocols

• What does MCP standardise between an AI application and an external service that a model's function-call format does not?
  • Knowledge · Asked at: Microsoft † · ✍ Answer
  • Tests: Whether protocol transport and capabilities are separated from model decisions.
  • A strong answer covers:
    • Separate the model's choice of a call from how a client discovers and reaches a server.
    • Name what the protocol fixes: transport, capability discovery, and typed tools, resources and prompts.
    • Discuss the trust boundary: a connected server supplies both capabilities and untrusted content.
  • Read: Model Context Protocol specification (Model Context Protocol) · Security Best Practices (Model Context Protocol)

Fine-tuning and post-training

• Choose between prompt changes, retrieval and finetuning for a failing AI feature; include data freshness, latency and total cost.
  • Applied scenario · Asked at: Databricks †, Microsoft †, OpenAI † · ✍ Answer
  • Tests: Whether the intervention matches the observed failure mode.
  • A strong answer covers:
    • Classify missing knowledge, poor instructions and unwanted behaviour separately.
    • Establish a prompt baseline; test retrieval for fresh attributable knowledge.
    • Use finetuning when representative examples justify it, then compare lifecycle cost and held-out quality.
  • Read: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., arXiv) · LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., arXiv)

AI system design

• Design a knowledge assistant over ten million enterprise documents with per-user permissions and a continuously changing corpus.
  • System design · Asked at: Amazon †, Databricks †, Microsoft †, OpenAI † · ✍ Answer
  • Tests: Whether ingestion, retrieval, access control and serving form an operable system.
  • A strong answer covers:
    • Clarify query volume, freshness and permission semantics before sizing.
    • Separate ingestion and index versions from query-time authorisation and retrieval.
    • Evaluate grounded answers and test revocation, stale data and partial outages.
  • Read: Document-level access control (Microsoft Learn) · Contextual Retrieval in AI Systems (Anthropic)
• Design a meeting assistant that records discussion, distinguishes speakers and publishes summaries and action items to connected tools.
  • System design · Asked at: Microsoft †
  • Tests: Whether audio processing, attribution and integration reliability fit together.
  • A strong answer covers:
    • Separate transcription from diarisation; map speakers to people using per-participant audio channels where available.
    • Link each action item to its transcript span, owner and confidence before anything is published.
    • Publish with an idempotency key per item so retries never duplicate tasks, and let users confirm first.
  • Read: Robust Speech Recognition via Large-Scale Weak Supervision (Radford et al., arXiv) · Designing robust and predictable APIs with idempotency (Stripe)

AI product strategy and metrics

• How would you prioritise features for an AI writing assistant in Microsoft Word?
  • Product case · Senior · Asked at: Microsoft † · ✍ Answer
  • Tests: Whether prioritisation accounts for user value, reliability and workflow fit.
  • A strong answer covers:
    • Segment writing tasks and identify friction in the existing workflow.
    • Compare expected value, quality risk, engineering cost and dependencies.
    • Choose a narrow first release and measure accepted, useful assistance.
  • Read: Measuring the User Experience on a Large Scale: User-Centered Metrics for Web Applications (Rodden, Hutchinson and Fu, Google Research) · Rules of Machine Learning (Google for Developers)
• How would you measure success for an AI feature in a Microsoft product?
  • Product case · Senior · Asked at: Microsoft † · ✍ Answer
  • Tests: Whether metrics connect model behaviour to user and business outcomes.
  • A strong answer covers:
    • Name the user goal and an observable successful task.
    • Separate adoption, retention, task quality, latency and cost.
    • Use a baseline or experiment and guard against metric gaming.
  • Read: Measuring the User Experience on a Large Scale: User-Centered Metrics for Web Applications (Rodden, Hutchinson and Fu, Google Research)
• Which business use cases suit AI agents, and how would you compare them?
  • Product case · Senior · Asked at: Microsoft † · ✍ Answer
  • Tests: Whether you distinguish the value of autonomous action from its operational cost.
  • A strong answer covers:
    • Score candidates on volume, a verifiable outcome, tolerance for error and reversibility.
    • Separate the value of the action taken from the cost of supervising and correcting it.
    • Compare against the cheaper option: a prompt, a fixed workflow or ordinary automation.
  • Read: Building Effective AI Agents (Anthropic)
• Explain the product trade-offs in choosing a model.
  • Knowledge · Senior · Asked at: Microsoft † · ✍ Answer
  • Tests: Whether model selection considers task quality, cost, latency and constraints together.
  • A strong answer covers:
    • Fix the task and a quality bar first, then measure candidate models on your own data.
    • Compare cost per successful outcome and latency as the user experiences it.
    • Add the constraints that disqualify a model: deployment region, data handling, change over time.
  • Read: Reasoning models (OpenAI API documentation)

Programmes and delivery

• Describe an AI product you built from scratch and how you took it through delivery.
  • Self-presentation · Senior · Asked at: Microsoft †
  • Tests: Whether you can explain a complete delivery path rather than only a prototype.
  • A strong answer covers:
    • Pick a product that reached real users, and open with the problem and why AI fitted it.
    • Spend most of the answer on the prototype-to-production gap: evaluation set, quality bar, failure handling, launch decision.
    • Close with adoption and outcome after launch, and how quality was monitored and improved once real traffic arrived.
  • Read: Your AI Product Needs Evals (Hamel Husain)

Behavioral and values

• Describe a technical decision you championed that turned out to be wrong.
  • Behavioral · Senior · Asked at: Microsoft †
  • Tests: Whether you can reverse a position and repair its consequences.
  • A strong answer covers:
    • Pick a decision you persuaded others to adopt, so that reversing it cost you something publicly.
    • Name the signal that showed it was wrong, how long you took to accept it, and why.
    • Describe how you reversed it openly, repaired what it had broken, and changed how you champion decisions.
  • Read: Postmortem Culture: Learning from Failure (Google)

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