Published on

FAANG Interview – Machine Learning System Design

Authors

Intro

In this post, I will cover the basic structure of the machine learning system design interview at FAANG, how to answer it properly, and study resources. Note that regular system design and ML system design are two different types of interviews at FAANG.

Who encounters ML System Design interviews?

  • academic ML researchers who want to transition to industry
  • industry ML researchers who are strong in theoretical knowledge and can contribute as a consultant to a category of hard problems
  • ML masters or PhDs
  • ML practitioners with industry experience – When it's unclear from a resume if the candidate has worked on ML problems, the candidate often has to conduct a regular system design and ML system design interview.
  • candidates with unclear resumes listing ML courses and projects

When is the ML system design interview?

This interview is typically conducted in the onsite/second stage round that occurs after passing the initial phone screen.

What questions are asked in an ML system design interview?

The field of AI/ML is vast and growing. FAANG interviewers do not expect a candidate to know the entirety of this growing field, but they do need to check and test the candidate's general understanding across the following major areas in ML:

  • problem exploration
  • train/eval data strategy
  • modeling
  • feature engineering
  • ML architecture
  • evaluation and deployment

Due to the vastness of the field, interviewers will study the candidate's resume and tailor the ML system design interview around the candidate's experience so that they can extract reliable "signal" about the candidate's abilities. After all, it doesn't serve the interviewer, the company, nor the interviewee any good if the interview is structured around theories and concepts that the interviewee did not even claim to possess on their resume.

How are candidates evaluated?

Candidates are evaluated along the following axes:

Problem exploration

Just like with regular system design interviews, candidates need to proactively explore the problem space, ask for and develop product requirements, and consider multiple ML solutions to a problem. More junior candidates often jump straight to feature engineering or focus exclusively on technical details without business context, so problem exploration is often an axis used to grade a candidate's seniority. Here's a structured framework for assessment:

Business Understanding – The candidate should begin by thoroughly understanding the business context and goals. This includes defining clear success metrics, both in terms of business KPIs and ML performance metrics. A strong candidate will demonstrate deep comprehension of product constraints and requirements before considering technical solutions.

Technical Approach – After establishing the business context, candidates should systematically explore potential ML solutions. The focus should be on drawing clear connections between business needs and ML decisions, while carefully considering system architecture trade-offs. This exploration should be methodical and well-reasoned, not jumping directly to implementation details.

Risk Assessment – A comprehensive problem exploration includes identifying potential challenges, understanding edge cases, and recognizing implementation constraints.

Below are the level-specific expectations:

Entry Level – PhD new grads demonstrate basic problem definition capabilities, usually identifying a single ML task before proposing a solution path with which they're familiar. Their approach tends to be more theoretical but should still show logical reasoning. Industry practitioners often draw from their practical background, connecting the current problem to relevant past experiences.

Senior – Senior engineers should demonstrate comprehensive success metric definitions and the ability to design multi-component ML systems. They should be comfortable analyzing trade-offs for complex objectives and capable of deep dives into specific components. Their solutions often integrate multiple ML tasks, such as combining candidate selection with ranking in recommendation systems, or implementing measurement, detection, and enforcement in integrity systems.

Staff+ – Staff engineers and above must exhibit all senior engineer capabilities while adding deeper product requirement analysis and broad industry awareness. They should demonstrate comparative analysis of different approaches, identify risks in complex designs, and make strategic architectural decisions. Their focus extends to system-level optimization and long-term sustainability.

Train/Eval Data Strategy

A critical aspect of ML system design interviews is assessing how candidates approach training data collection and utilization in production environments. The core assessment focuses on whether candidates can devise effective strategies for training data collection and management.

Data Collection & Labeling – Candidates should demonstrate the ability to identify appropriate data sources and understand various labeling methodologies. This includes leveraging explicit user feedback mechanisms, implicit behavioral signals, human rater systems, and active learning approaches.

Quality Control – Candidates should recognize common biases such as position bias in ranking systems, selection bias in user interactions, and existing ranking system bias. They should demonstrate deep understanding of label consistency challenges and propose appropriate quality validation methods. This includes strategies for maintaining data quality over time and across different user segments.

Cold Start Considerations – Candidates should understand explore/exploit trade-offs and propose concrete solutions for gathering initial training data. This includes consideration of both technical and business constraints in the bootstrapping process.

Below are the level-specific expectations:

Entry level – Entry level engineers should demonstrate clear understanding of basic training data requirements. This includes defining positive and negative examples for classification tasks and proposing at least one well-justified data collection approach. They should show basic understanding of data quality requirements and common pitfalls.

Senior – Senior engineers must show deeper expertise through comparative analysis of different data collection methods. They should demonstrate sophisticated handling of data noise and strategic use of user engagement signals. Their solutions should address basic cold-start scenarios and show clear understanding of trade-offs between different signal types. They should be able to design robust data collection pipelines.

Staff+ – Staff engineers must exhibit mastery through advanced cold-start solutions in business contexts. They should demonstrate expertise in implementing explore/exploit strategies and designing active learning systems. Their solutions should include recognition and mitigation of feedback loops, along with comprehensive data quality frameworks. They should show ability to design and evolve complex data strategies over time.

Red flags – Common red flags include over-reliance on standard train/test splits and assumptions of Kaggle-like prepared datasets. Candidates who fail to consider production constraints, neglect data quality measures, or show insufficient attention to cold-start problems raise concerns. The absence of practical considerations for real-world implementation is a significant warning sign.

Subscribe to receive future guides on FAANG interview preparation. Unsubscribe any time.

Feature Engineering

Feature engineering is a critical component of ML system design interviews, focusing on a candidate's ability to identify, design, and implement effective features for specific ML tasks. The evaluation centers on whether candidates can move beyond generic solutions to create meaningful, task-specific features that align with both technical constraints and business requirements.

Feature Ideation and Structure – Rather than simply listing features, strong candidates demonstrate structured thinking about feature categories. They should organize features into logical groups such as user characteristics, content attributes, and interaction patterns. The focus should extend beyond obvious choices like demographic data, which nearly every candidate mentions.

Task-Specific Relevance – Candidates should demonstrate the ability to identify features that are particularly important for the specific task at hand. For example, when designing a recommendation system, they might focus on detailed breakdowns of past user interactions rather than generic features that could apply to any ML problem.

Below are the level-specific expectations:

Entry level – New PhD graduates typically propose more theoretical features but should demonstrate the ability to explain their predictive value. While they may need guidance to develop richer features, they should understand basic feature representation for both numerical and categorical features. Industry new grads often show more practical feature ideation but may lack depth in advanced representations like categorical features.

Senior Engineers – Senior engineers should demonstrate strong product intuition in feature selection and familiarity with common ML feature representation methods. They should be comfortable with high-dimensional features across domains like computer vision, text embedding, and categorical features. Domain expertise should be evident - NLP specialists should contrast approaches like TF/IDF, word2vec, and BERT, while ranking specialists should demonstrate deep understanding of engagement history features.

Staff+ Engineers – Staff+ engineers must show mastery in feature organization and selection specific to product requirements. They should discuss sophisticated trade-offs regarding model evaluation and performance impacts, particularly with complex features like categorical feature interactions and large embeddings. Their expertise should extend to building complex submodels and scaling large feature systems. Additionally, they often identify novel product opportunities that could generate new feature signals.

Red Flags – Candidates should avoid proposing features that don't align with end-to-end solution requirements. For example, suggesting features requiring complex preprocessing for simple logistic regression, or using high-dimensional categorical features with insufficient training data. The key is maintaining consistency between feature complexity, data availability, and model architecture.

Model Architecture & Training

The modeling section of ML system design interviews evaluates a candidate's ability to select, justify, and optimize model architectures while demonstrating deep understanding of their inner workings. The core assessment focuses on whether candidates can identify root causes of model issues and propose effective solutions.

Model Selection and Justification – Strong candidates don't simply default to familiar models from previous projects or suggest trying everything to see what works best. Instead, they should justify architectural choices based on the problem constraints, data characteristics, and business requirements. While specialization in specific architectures (like neural networks) is acceptable, candidates should demonstrate deep understanding of their chosen approach.

Technical Depth – Knowledge should extend beyond surface-level framework familiarity (like Keras API calls) to fundamental understanding of model mechanics. This includes comprehension of regularization, optimization, and architectural trade-offs.

Below are the level-specific expectations:

Entry level – PhD graduates typically exhibit deep theoretical knowledge of specific model architectures, including optimization techniques and regularization approaches. They should articulate how these components work together and affect model behavior. Industry new grads often show stronger practical alignment between model choice and feature design, though they may have limited depth in optimization details. They should understand common hyperparameter tuning approaches, even if they lack deep optimization knowledge.

Senior Engineers – Senior engineers must justify model selections based on multiple factors including latency requirements, accuracy needs, and data complexity. They should demonstrate either broad knowledge across classical approaches with depth in one area, or comprehensive understanding of neural network architectures including layers, normalization methods, regularization techniques, and loss functions. Their decisions should reflect practical implementation considerations.

Staff+ Engineers – Staff engineers should meet senior engineer requirements while also demonstrating mastery of advanced topics. This includes technical solutions for exploration/exploitation challenges, cold start problems, and sophisticated approaches like reinforcement learning and semi-supervised learning. They should show ability to design novel architectural solutions for complex business problems.

Model Evaluation Strategy

Model evaluation in ML system design interviews assesses a candidate's ability to determine model effectiveness through both offline metrics and online experimentation. The core focus is whether candidates can effectively measure model performance and compare different approaches while maintaining alignment with business objectives.

Offline Evaluation – Strong candidates should demonstrate more than superficial knowledge of evaluation metrics. They should understand metric properties deeply and articulate clear reasoning for metric selection based on problem requirements. This includes understanding the implications of data imbalance, edge cases, and potential biases in offline evaluation.

Online Experimentation – A/B testing knowledge should extend beyond basic setup to include understanding of practical challenges in production environments. Candidates should demonstrate ability to design meaningful experiments and interpret results in the context of business impact.

Below are the level-specific expectations:

Entry level – New PhD graduats or junior industry practitioners should demonstrate fundamental understanding of A/B testing principles, even with limited practical experience. They should be able to identify appropriate metric sets for specific problems (such as precision-recall trade-offs) and show awareness of common evaluation challenges like imbalanced datasets. Their solutions might include simple workarounds for these challenges.

Senior Engineers – Senior engineers must demonstrate comprehensive understanding of metric selection and justification. They should show deep familiarity with metric properties and extensive A/B testing experience. Their expertise should extend to monitoring and debugging online behaviors, including performance variance analysis, problem-specific degradation patterns, and alert system design. They should articulate clear reasoning for choosing specific metrics over alternatives.

Staff+ Engineers – Staff+ engineers should exhibit mastery of industry-standard evaluation approaches while demonstrating ability to innovate in measurement strategy. They should proactively connect metrics to product and business goals while addressing sophisticated challenges in A/B testing such as dilution effects, biases, and feedback loops. Their expertise extends to advanced measurement approaches like prevalence measurement and deletion testing. They often demonstrate knowledge of sophisticated evaluation frameworks, such as treating recommender systems as contextual bandits to measure incremental value and account for feedback loops.

Study Material and Interview Practice Problems

Machine Learning System Design Interview, https://amzn.to/3UGTznJ – As mentioned earlier in this guide, this book is a must if you need to prepare for an interview that's coming up in a week or more. It contains 10 problems you'll commonly encounter in ML interviews, and the book even provides its own framework for solving any ML system design question as well as 200+ visual diagrams explaining the solutions.

Airbnb Tech Blog ML Articles – This easy-to-read engineering tech blog from Airbnb contains the designs of many ML systems you'll encounter in interviews and on the job. It covers the practical application of embeddings, diversity in ranking, graph machine learning, search ranking, and more. If you're short on time, I highly recommend skimming through this blog and studying the overall designs. If you have weeks or months to prepare, spend a day or two diving deeper into each article to internalize their designs because they contain common concepts and components you're likely to use in your ML interview.

Netflix Tech Blog – The Netflix tech blog also has a good selection of ML specific articles that cover common designs. I highly recommend reading these articles: Recommending for Long-Term Member Satisfaction at Netflix, Detecting Speech and Music in Audio Content, and Building In-Video Search. There are many more ML related articles so browse through the blog to learn more ML designs.

Concluding Remarks

I hope you found this guide to ML system design interview preparation helpful. Remember, interviewing is like any other skill – it can be learned. Use the tips and advice in this guide to uplevel your ML system design interview preparation to land your next ML role.