Glean (CA) Machine Learning Engineer Interview Guide 2026

What is a Machine Learning Engineer?

As a Machine Learning Engineer at Glean, you are not just building models; you are architecting the intelligence layer of the world’s most powerful work assistant. Glean’s mission is to bring the power of search and discovery to the enterprise, connecting fragmented data across SaaS applications into a unified, intuitive experience. In this role, you sit at the intersection of systems engineering, natural language processing (NLP), and information retrieval.

Your work directly impacts how users find information and answers within their organizations. You will tackle complex challenges such as semantic search, personalized ranking, Retrieval-Augmented Generation (RAG), and large-scale vector indexing. Unlike roles where ML is a peripheral optimization, at Glean, your contributions drive the core product experience. You will be responsible for the end-to-end lifecycle of these systems—from researching novel deep learning techniques to deploying highly performant models that serve queries in milliseconds.

The environment is fast-paced and technically rigorous. You will join a team of engineers who have previously built core search and infrastructure at companies like Google and Facebook. This position offers a rare opportunity to work on the bleeding edge of Generative AI and enterprise search, solving problems that require both deep theoretical knowledge and practical engineering excellence.

Common Interview Questions

The following questions are representative of what you might face. They are not an exhaustive list but serve to illustrate the types of problems we ask.

Coding & Algorithms

"Given a string representing a simplified code block, validate its syntax."
"Implement a function to parse a log file and extract specific error patterns."
"Given a list of query logs, find the top K most frequent queries in the last hour."
"Serialize and deserialize a binary tree."
"Find the longest substring with at most K distinct characters."

ML System Design

"Design a type-ahead (autocomplete) system for a search bar."
"How would you design a system to detect duplicate documents across different file types (PDF, Docx, HTML)?"
"Design a personalized news feed for a professional network."
"How would you architect a semantic search system using vector embeddings?"

ML Theory & Concepts

"What is the difference between Word2Vec and BERT embeddings?"
"Explain the vanishing gradient problem and how to mitigate it."
"How does a Transformer's self-attention mechanism scale with input length?"
"What metrics would you use to evaluate a ranking model for an enterprise search engine?"

See every interview question for this role

Practice questions from our question bank

Curated questions for Glean (CA) from real interviews. Click any question to practice and review the answer.

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate Cross-Validation Impact on Model Performance

Analyze how cross-validation affects the performance metrics of a regression model predicting housing prices.

Supervised Learning

Cross-Validation

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Machine Learning

Predict Machinery Failure Under Imbalance

Build an imbalanced binary classifier to predict machinery failure 24 hours ahead using sensor, maintenance, and usage data.

Supervised Learning

Cross-Validation

Feature Engineering

Easy

NLP

Explain Context Processing in LLMs

Build a transformer-based demo that explains tokenization, embeddings, self-attention, and next-token prediction for legal and technical text.

Neural Networks

Tokenization

Language Models

Medium

Model Evaluation

Detect Leakage in Feature Engineering

Diagnose whether feature engineering leakage caused a repeat-purchase model to fall from 0.95 to 0.69 AUC after deployment.

Cross-Validation

Calibration

Feature Engineering

Easy

Model Evaluation

Evaluate Metrics for Rare Player Behavior

Choose the right metrics for a model with 0.1% positives, where accuracy is misleading and threshold selection drives business value.

Precision

Recall

F1 Score

Medium

Machine Learning

Interpret Coefficients of Linear Regression Model

Explain the significance of coefficients in a linear regression model and their impact on predictions in a business context.

Regression

Medium

NLP

Extract Resume Skills from CVs

Build a transformer-based NER pipeline to extract and normalize skills from noisy resume text with high recall on technical skills.

Text Classification

Named Entity Recognition

Language Models

Medium

Model Evaluation

Version Data and Models Reliably

Design a production versioning strategy for data and models after campaign conversion fell from 3.8% to 3.1% and calibration worsened sharply.

Accuracy

Calibration

Threshold Tuning

Easy

NLP

Explain Transformer Design for News NLP

Explain Transformer architecture and why self-attention-based models outperform RNNs for news text understanding and classification.

Neural Networks

Language Models

Deep Learning

Hard

Pipelines

Design ML Serving Platform for Vehicle GPUs

Design a concurrent ML serving pipeline to run multiple perception models on a vehicle GPU with a max latency of 30ms.

Infrastructure

Medium

Model Evaluation

Diagnose Weekend Classification Drift

Diagnose why a support ticket classifier's urgent-ticket recall drops from 88% on weekdays to 57% on weekends and propose fixes.

A/B Testing

Threshold Tuning

Diagnosis

Medium

Model Evaluation

Design a Fair Cross-Hardware Benchmark

Redesign an LLM benchmark so latency, throughput, and quality are reproducible and fairly comparable across A100, H100, TPU v5e, and MI300X.

Accuracy

Precision

Recall

Medium

Machine Learning

Long-Tail Emergency Vehicle Detection

Design a long-tail classification strategy to detect rare emergency vehicles with high recall under tight on-device latency constraints.

Supervised Learning

Bias-Variance Tradeoff

Deep Learning

+1 more

Medium

NLP

Clinical Document QA: Fine-Tune vs RAG

Compare a fine-tuned LLM and a RAG pipeline for answering clinician questions over internal clinical documents with grounded citations.

Word Embeddings

Text Classification

Language Models

Medium

Model Evaluation

Assess Offline NDCG Impact on User Reading Time

Evaluate whether a 5% increase in NDCG correlates with a rise in user reading time for a content recommendation system.

Accuracy

Precision

Recall

Hard

Machine Learning

Harmful Video Upload Detection Pipeline

Design a multimodal classifier to detect harmful uploaded videos with extreme class imbalance and strict 30s latency and safety recall targets.

Supervised Learning

Deep Learning

Feature Engineering

Medium

Model Evaluation

Evaluate Distributed Inference Scaling Metrics

Evaluate distributed inference using throughput, latency, utilization, strong/weak scaling, and Amdahl’s law, then diagnose why 64-GPU scaling is inefficient.

Accuracy

Precision

Recall

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These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Getting Ready for Your Interviews

Preparing for the Machine Learning Engineer interview at Glean requires a balanced focus on strong algorithmic foundations and practical ML system design. The bar is high, and the process is designed to identify engineers who can navigate ambiguity and deliver robust code. You should approach your preparation with the mindset of a systems builder, not just a researcher.

Key Evaluation Criteria

Algorithmic Proficiency & Implementation – You must demonstrate the ability to write clean, bug-free code for complex problems. Interviewers look for candidates who can handle "messy" requirements, such as heavy string parsing or complex input formatting, without getting bogged down. It is not enough to know the logic; your implementation speed and code structure are critical.

ML System Design – You will be evaluated on your ability to design scalable machine learning systems from scratch. We look for candidates who can define the problem, select appropriate metrics, design the data pipeline, choose the right modeling approach (e.g., embeddings vs. keywords), and discuss serving constraints. We value practical trade-offs over buzzwords.

Domain Expertise (NLP/Search) – Given our product, deep familiarity with modern NLP (Transformers, LLMs) and Information Retrieval (ranking, indexing) is a significant differentiator. You should be able to explain the "why" behind your model choices and discuss how you handle data sparsity or domain adaptation.

Engineering Ownership – Beyond technical skills, we assess your ability to own a problem. This includes how you communicate with stakeholders, how you handle vague requirements, and your proactive approach to debugging and testing. We value engineers who drive projects forward independently.

Interview Process Overview

The interview process for the Machine Learning Engineer role generally follows a standard but rigorous structure. It typically begins with a recruiter screen or a direct hiring manager call, followed by a technical phone screen. If you pass the initial screen, you will move to the onsite loop (virtual), which consists of multiple rounds focusing on coding, system design, and behavioral alignment.

Candidates often describe the process as challenging, with a difficulty rating ranging from medium to hard. The coding rounds can be intense, often involving LeetCode Medium/Hard problems that may require significant implementation effort (e.g., parsing strings or handling complex data structures). Unlike some companies that focus purely on algorithmic tricks, Glean interviews often test your ability to write working code for practical, slightly verbose problems.

The atmosphere aims to be collaborative, but experiences can vary. You should expect interviewers to be focused on your output. It is crucial to be proactive in your communication, especially if the interviewer is quiet. The process is designed to test your technical limits, so maintaining composure when stuck is just as important as finding the optimal solution.

The visual timeline above outlines the typical progression from application to offer. Use this to plan your study schedule: prioritize coding speed and accuracy for the early stages, then shift your focus to high-level system design and resume deep-dives as you approach the onsite loop. Note that the "Recruiter Screen" and "Hiring Manager Screen" are sometimes combined or swapped depending on the team's immediate needs.

Deep Dive into Evaluation Areas

To succeed at Glean, you need to demonstrate depth in specific technical areas. Based on candidate feedback and our engineering culture, the following areas are the pillars of our assessment.

Algorithmic Coding & Implementation

This is the most frequent filter in our process. You must be comfortable translating logic into code quickly. We value "functional" coding ability—meaning you can handle input/output operations, string manipulation, and edge cases, not just abstract dynamic programming.

Be ready to go over:

String Manipulation & Parsing – We often ask questions that involve parsing complex input strings or formatting output. This tests your attention to detail and familiarity with standard libraries.
Graph & Tree Traversal – DFS/BFS, topological sorting, and finding paths in grids or networks are common themes.
Data Structures – Heavy use of HashMaps, Heaps, and Stacks to optimize performance.
Advanced concepts – Tries (prefix trees) for search-related problems and sliding window techniques.

Example questions or scenarios:

"Parse a simplified HTML string and return a specific structure."
"Implement a basic calculator that handles nested parentheses and different operators."
"Find the shortest path in a grid with specific obstacles and movement rules."

Machine Learning System Design

In this round, you will design a real-world ML system, likely related to search, recommendation, or ranking. We want to see how you bridge the gap between a business problem and a technical solution.

Be ready to go over:

Search & Ranking Systems – How to build a retrieval system (inverted index vs. vector search) and a ranking layer (learning to rank).
Recommendation Engines – Collaborative filtering, matrix factorization, and two-tower architectures.
Data Pipelines – Handling training data generation, feature engineering, and dealing with class imbalance.
Evaluation Metrics – Precision/Recall, NDCG, MRR, and online A/B testing metrics.

Example questions or scenarios:

"Design a document search engine for an enterprise company."
"Build a system to recommend relevant Slack threads to a user based on their current project."
"How would you improve the relevance of search results for queries with low click-through rates?"

Resume Deep Dive & ML Theory

Expect a round dedicated to scrutinizing your past projects. Interviewers will dig into the specific decisions you made. They want to ensure you understand the theoretical underpinnings of the tools you used, rather than just importing libraries.

Be ready to go over:

Model Architecture – Why did you choose a Transformer over an LSTM? Why that specific loss function?
Training Dynamics – How did you handle overfitting? What optimization techniques did you use?
NLP Specifics – Tokenization strategies, embeddings (Word2Vec, BERT, RoBERTa), and fine-tuning LLMs.

Example questions or scenarios:

"Walk me through the most complex model you deployed. What were the latency constraints?"
"Explain how the attention mechanism works in the Transformer architecture."
"How did you validate your model offline before pushing it to production?"

The word cloud above highlights the most frequently discussed concepts in our interviews. You will notice a strong emphasis on Search, Design, Parsing, and Ranking. This indicates that while general ML knowledge is required, applying that knowledge to Information Retrieval and System Design is the key to passing the onsite loop.

Key Responsibilities

As a Machine Learning Engineer at Glean, your daily work revolves around making enterprise data accessible and useful. You will spend a significant portion of your time designing and implementing models that power our core search and "Work Assistant" features. This involves working with massive datasets of unstructured text from various enterprise sources (Jira, Confluence, Google Drive, etc.).

You will be responsible for the full lifecycle of these models. This means you will not only train models but also write the production C++ or Go code to serve them efficiently. You will collaborate closely with backend engineers to ensure your models fit within our latency budgets. Experiments are a constant part of the role; you will run A/B tests to validate improvements in ranking quality and answer relevance.

Additionally, you will drive initiatives in Generative AI. This includes leveraging Large Language Models (LLMs) to summarize documents, generate answers to complex natural language queries, and synthesize information across different platforms. You are expected to stay current with the latest research in NLP and bring those advancements into the product.

Role Requirements & Qualifications

We are looking for engineers who combine strong software engineering fundamentals with deep ML expertise.

Technical Skills
- Must-have: Proficiency in Python for modeling and C++, Java, or Go for production systems.
- Must-have: Deep understanding of Machine Learning frameworks (PyTorch, TensorFlow) and libraries (HuggingFace, Scikit-learn).
- Must-have: Solid background in NLP, Information Retrieval, or Recommendation Systems.
- Nice-to-have: Experience with Vector Databases, RAG architectures, and distributed training.
Experience Level
- Typically requires a BS, MS, or PhD in Computer Science or a related field.
- For this level, we generally look for candidates with industry experience deploying ML models at scale, though exceptional new graduates with strong internships are considered.
Soft Skills
- Ability to communicate complex technical concepts to non-experts.
- Strong product sense—understanding why we are building a feature, not just how.
- Resilience in debugging complex systems.

Frequently Asked Questions

Q: How difficult are the coding interviews? The coding rounds are generally considered Hard. Candidates often report questions that are algorithmically standard but implementation-heavy. You should be prepared to write significant amounts of code and handle data parsing tasks that go beyond simple algorithmic logic.

Q: Is the work purely research-based? No. This is an engineering-heavy role. While you will read papers and experiment with new architectures, you are expected to write production-quality code and own the deployment of your models.

Q: What is the culture like for the ML team? The culture is collaborative but intense. The team is composed of high-performers from top tech companies. There is a strong emphasis on autonomy and ownership. You are expected to identify problems and solve them.

Q: Will I meet with a Hiring Manager early in the process? Yes, often the first or second step is a call with a Hiring Manager. Be prepared for this—it is not just a casual chat. They will assess your background and potential fit immediately.

Q: What is the timeline for the process? The process can move quickly, often concluding within 2-3 weeks if scheduling aligns. However, due to the rigor of the rounds, feedback might take a few days after the onsite loop.

Other General Tips

Master the "Boring" Code: A common stumbling block for candidates at Glean is failing on questions that require string parsing or messy input handling. Do not just practice dynamic programming; practice manipulating data structures and formatting strings.

Clarify Everything: In coding rounds, requirements might be intentionally vague. Ask questions about input formats, edge cases, and constraints before you start writing code. This shows maturity and prevents you from going down the wrong path.

Interview Guides

Glean (CA)

What is a Machine Learning Engineer?

Common Interview Questions

Coding & Algorithms

ML System Design

ML Theory & Concepts

See every interview question for this role

Practice questions from our question bank

Sign up to see all questions

Getting Ready for Your Interviews

Interview Process Overview

Deep Dive into Evaluation Areas

Algorithmic Coding & Implementation

Machine Learning System Design

Resume Deep Dive & ML Theory

Key Responsibilities

Role Requirements & Qualifications

Frequently Asked Questions

Other General Tips

Note

Tip

Summary & Next Steps

See every interview question for this role