Glean Machine Learning Engineer Interview: Questions & Prep (2026)

Glean is an enterprise AI search company helping teams find information across tools like Slack, Google Drive, Jira, and Confluence. The product is built on semantic search, personalization, and generative AI, which means ML Engineers here typically work on embedding models, retrieval-augmented generation (RAG), search ranking, and LLM-based features.

With 146 open roles as of July 2026, Glean is in an active hiring phase across ML disciplines. Candidates typically report a structured process: a recruiter screening call, a technical round covering coding and ML fundamentals, an ML system design session, and a behavioural interview. The technical bar is high, and questions skew heavily toward information retrieval and applied LLM engineering rather than classical ML theory.

This guide covers the questions candidates report most often, sample answers in STAR format, and a structured preparation plan to help you walk in confident.

These questions are based on candidate reports and Glean's publicly known engineering focus areas. Expect depth on retrieval, RAG, and practical LLM work.

1. How would you improve search relevance for a large enterprise knowledge base?
2. Walk us through how you would design a RAG pipeline from scratch, including chunking, retrieval, and generation.
3. How do you evaluate and compare embedding models for a document retrieval task?
4. What are the trade-offs between dense (semantic) retrieval and sparse (keyword-based) retrieval, and when would you use each?
5. How would you add a personalization layer to a shared enterprise search system without degrading results for other users?
6. Describe how you would fine-tune an LLM for a specific enterprise domain when you have limited labelled data.
7. How do you decide what to optimize for when precision and recall pull in opposite directions?
8. Tell us about a time you debugged an ML model that started underperforming in production.
9. How would you design a system to support multi-modal search, combining text and image signals?
10. What techniques do you use to reduce inference latency when serving ML models at scale?
11. How would you address the cold-start problem for a new employee joining a personalized search system?
12. Walk us through how you would design and run an A/B test to validate a change to the search ranking model.

Q: How would you improve search relevance for an enterprise knowledge base?

*Situation:* At my previous company, our internal search tool returned results based purely on keyword frequency, which meant employees were missing relevant documents written using different terminology.

*Task:* I was asked to design and ship a better retrieval approach that could understand query intent, not just exact keyword matches.

*Action:* I evaluated several pre-trained bi-encoder models using a small set of manually labelled query-document pairs collected from user feedback tickets. I set up a vector index, built a hybrid pipeline combining sparse keyword signals with dense semantic embeddings, and added a cross-encoder re-ranker for the final step. I also worked with the data team to build an offline evaluation set so we could measure changes consistently before going live.

*Result:* Offline evaluation showed a clear improvement in ranking quality across our test queries. A pilot group of users reported finding what they needed noticeably faster, and we rolled out gradually while monitoring engagement before a full launch.

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Q: Tell us about a time you debugged an ML model that started underperforming in production.

*Situation:* A document ranking model I had shipped began producing worse results for a specific category of queries a few weeks after launch, and users were flagging it through support tickets.

*Task:* I needed to diagnose the root cause quickly and restore quality without waiting for a full retrain cycle.

*Action:* I pulled feature distributions from recent production logs and compared them to the training data. I found that a data pipeline change had shifted how one categorical feature was encoded, creating a mismatch between training and serving. I patched the encoding logic, validated the fix on a held-out evaluation set, and ran the corrected model in shadow mode before promoting it to production.

*Result:* Offline metrics returned to their pre-incident baseline after the fix. I also added automated data validation checks to the pipeline so that similar distribution drift would surface an alert rather than silently degrading results over time.

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Q: Walk us through how you would design a RAG pipeline from scratch.

*Situation:* My team decided to build a question-answering feature on top of internal company documents spanning multiple file types and sources.

*Task:* I was responsible for the end-to-end design of the retrieval component and its integration with the generation model.

*Action:* I structured the work into stages: document ingestion and chunking, embedding and indexing, retrieval combining keyword and semantic signals, re-ranking with a cross-encoder, and finally passing retrieved context to the LLM with a carefully designed prompt. I evaluated multiple chunking strategies and found that overlapping chunks worked better for long documents. I also built an offline evaluation harness using a curated set of question-answer pairs so each stage could be improved independently.

*Result:* The system handled the majority of queries in our test set correctly. We launched to a pilot group, collected structured feedback, and used it to improve the chunking and re-ranking stages in subsequent iterations.

For ML system design questions, structure your answer in four layers: problem framing (what is the input, output, and success metric), data and training (how you get labels and what features matter), model and architecture (what you would build and why), and serving and monitoring (latency targets, quality signals, how you detect drift). Cover all four layers before going deep on any one.

Glean's domain means retrieval and ranking questions come up constantly. When answering these, always mention both offline metrics (NDCG and MRR are industry-standard retrieval evaluation measures) and online metrics (click-through rate, time-to-find). Interviewers want to see you connect model choices to real user impact, not just benchmark scores.

For behavioural questions, use the STAR format cleanly: Situation (set the context briefly), Task (your specific responsibility), Action (what you did, with enough detail to feel real), Result (what changed and how you measured it). Keep Situation and Task short. Spend most of your time on Action and Result.

For debugging and production questions, show a systematic process: check the data pipeline first, then feature distributions, then model behaviour, then serving infrastructure. Jumping straight to 're-train the model' without checking upstream data signals shallow experience to most interviewers.

For trade-off questions, name the trade-off explicitly, give a concrete example of when you would lean each way, and connect your answer to Glean's specific context: enterprise search, latency-sensitive serving, and personalization needs.

Depth in information retrieval. Glean's core product is search. Interviewers want to see that you understand retrieval beyond the basics: the difference between sparse and dense approaches, how re-ranking works, what evaluation metrics like MRR and NDCG actually measure, and how to handle edge cases like rare queries or very short documents.

Practical LLM and RAG experience. Knowing transformer architecture theory is not enough. Interviewers want to hear about real decisions you made: how you chose a chunking strategy, which embedding model you selected and why, how you handled context length limits, and how you evaluated generation quality in a way you could track over time.

A metric-first mindset. Every answer about improving a system should explain how you would know if it worked. Glean deals with enterprise users who have high expectations, and vague claims of 'better results' do not land. Talk about offline evaluation sets, A/B tests, and user satisfaction signals as natural parts of your workflow.

Clear communication under pressure. Candidates report that Glean values people who can explain complex ideas simply. Practice narrating your thought process out loud during system design, especially when you are still working through the problem. Walking the interviewer through your reasoning as you go is a stronger signal than thinking silently and then presenting a finished answer.

Ownership and follow-through. Behavioural answers that show you shipped something, measured it, iterated on feedback, and improved it over time are stronger than answers that end at the point of building the model.

Week 1: Retrieval and search fundamentals

Review how sparse keyword retrieval works and where it falls short for semantic understanding. Study bi-encoder and cross-encoder architectures for dense retrieval. Understand how vector databases index and serve embeddings at scale. Practice explaining the trade-offs between sparse and dense retrieval out loud, as you will very likely be asked to compare them.

Week 2: RAG, LLMs, and fine-tuning

Build or review a simple RAG pipeline end-to-end: chunking, embedding, retrieval, and generation. Study chunking strategies for long documents, including fixed-size and overlapping approaches. Review parameter-efficient fine-tuning methods so you can discuss them clearly. Understand how to evaluate RAG outputs both offline (using labelled question-answer pairs) and with live user signals.

Week 3: ML system design practice

Practice designing a search ranking system from scratch, covering data collection, model training, serving, and monitoring. Then design a personalization layer for an enterprise search product. Time yourself and aim to cover problem framing, architecture choices, and trade-offs within a single structured session.

Week 4: Coding, behavioural prep, and Glean research

Solve coding problems focused on data structures and algorithms that come up in ML engineering contexts. Prepare STAR stories for at least three scenarios: debugging a production issue, shipping under pressure, and working across teams. Read Glean's engineering blog and any publicly available talks from their ML team. Note what excites you about their specific product and be ready to say it naturally in conversation.

If you are juggling prep alongside an active job search, knok checks 150+ job sites nightly, applies to roles that match your resume, and messages HR for you, so you can keep your energy focused on interview practice rather than application logistics.

Preparing for a generic ML interview instead of a search-focused one. Glean's questions are grounded in retrieval, ranking, and generation. Candidates who prepare only on classical ML topics like gradient boosting or image classification are often caught off guard by the depth of retrieval questions.

Answering retrieval questions with theory only. Saying 'I would use embeddings' without explaining how you would evaluate them, which vector database you would choose, or how you would handle retrieval failures signals surface-level experience.

Skipping evaluation in system design. Every architecture description needs a clear answer to 'how would you know this is working?' Candidates who describe a full system design without mentioning evaluation metrics or testing strategies tend to score lower on the design dimension.

Weak STAR structure in behavioural interviews. A common pattern is spending most of the answer on Situation and Task, then rushing through Action and Result. Interviewers care most about what you specifically did and what measurably changed as a result.

Arriving without questions for the interviewer. Glean is at an interesting point in the enterprise AI market. Asking thoughtful questions about their technical challenges, how they evaluate generative features, or how the ML team is structured signals genuine interest and leaves a strong closing impression.