Introduction

Welcome to your preparation guide for Meta’s In-Domain Design interview. This interview is tailored to assess your expertise in a specific domain relevant to the role you’re applying for. Unlike the general system design interview, the In-Domain Design interview focuses on your deep knowledge and experience in a particular technical domain.

This guide will help you understand what to expect, how to prepare, and the key strategies for success in Meta’s In-Domain Design interviews across various engineering domains.

What is In-Domain Design Interview?

Overview

The In-Domain Design interview at Meta is a specialized system design interview that evaluates:

  • Your thought process and approach when working in a domain you’re familiar with
  • Your knowledge and experience in the specific domain
  • Your ability to design systems or components within that domain
  • How you handle ambiguity and solve problems using domain-specific knowledge

Key Characteristics

  • Domain-Specific: Focuses on your expertise area (e.g., Mobile, Backend, Infrastructure, ML, etc.)
  • Design-Focused: Majority of the interview assesses design skills
  • Knowledge-Based: Small portion evaluates your domain knowledge
  • Problem-Solving: Tests your ability to solve problems in your domain
  • Collaborative: Interactive discussion with a Meta engineering leader

Interview Format

Structure

  • Duration: 45 minutes
  • Format: Solve one problem and design a component/system in your domain
  • Interviewer: Meta engineering leader familiar with your domain
  • Focus:
    • Design skills (majority of interview)
    • Domain knowledge (small portion)
    • Problem exploration and clarification
    • Trade-off analysis

Interview Flow

  1. Introduction (2-3 minutes)
    • Brief introductions
    • Overview of the interview format
  2. Problem Presentation (5 minutes)
    • Interviewer presents the problem
    • You should ask clarifying questions
  3. Problem Exploration (10-15 minutes)
    • Gather requirements
    • Clarify uncertainties
    • Define the problem scope
  4. Design Discussion (20-25 minutes)
    • High-level design
    • Detailed design
    • Trade-off discussions
    • Deep dive into specific areas
  5. Wrap-up (2-3 minutes)
    • Summary of the design
    • Any final questions

Quick Tips

What Meta is Looking For

  • Domain Expertise: Deep knowledge in your specific domain
  • Design Thinking: Ability to design systems/components in your domain
  • Problem-Solving: Using domain knowledge to solve unfamiliar problems
  • Communication: Clear explanation of your thought process
  • Trade-off Analysis: Understanding pros/cons of different approaches

Important Points

  1. You Don’t Need to Know Everything: You should know enough to weigh design considerations and know when to consult an expert
  2. No Perfect System Expected: Meta doesn’t expect you to design the perfect system or have the answer ready immediately
  3. Ambiguity is Intentional: Questions have built-in ambiguity to test your problem-solving approach
  4. Use Your Domain Knowledge: Apply your existing domain knowledge to tackle problems, even in unfamiliar areas
  5. Ask Questions: Don’t panic if something is unclear - asking questions is part of the assessment

Interview Success Factors

  1. Ask Clarifying Questions: Don’t hesitate to ask questions to understand the problem
  2. Show Your Thought Process: Explain your reasoning at each step
  3. Demonstrate Domain Knowledge: Use your domain expertise to inform your design
  4. Discuss Trade-offs: Always consider pros and cons of different approaches
  5. Be Collaborative: Engage with the interviewer and adjust based on feedback

Interview Preparation Framework

Meta evaluates candidates across 6 key areas. Use these areas to structure your interview responses:

1. Problem Exploration

Don’t be afraid to ask questions!

Key Actions:

  • Ask about the underlying motivation: “What is the underlying motivation of building ‘X’?”
  • Gather requirements and clarify uncertainties:
    • Target users and use cases
    • Scale and performance requirements
    • Constraints (technical, business, resource)
    • Integration points with existing systems
    • Success criteria
  • Define the problem: Attempt to define the problem and how “X” will be built (this gives you a roadmap for the remainder of the interview)

Example Questions to Ask:

  • What is the scale we’re designing for?
  • What are the performance requirements (latency, throughput)?
  • What are the constraints (memory, CPU, network, storage)?
  • Who are the target users?
  • What are the success criteria?
  • Are there any existing systems we need to integrate with?
  • What hardware/platforms need to be supported?

2. Design Approach

Provide a high-level overview and reasoning

Key Actions:

  • Provide a high-level overview of the main components of your design
  • Summarize your reasoning why these components are relevant
  • Consider different options to arrive at a solution
  • Think about how this product/system will impact other areas of the business

What to Include:

  • High-level architecture diagram (draw it out)
  • Main components and their responsibilities
  • Data flow through the system
  • Integration points with other systems
  • Impact on other systems/services
  • Domain-specific considerations

Domain-Specific Considerations:

  • Mobile: Platform-specific APIs, battery optimization, network efficiency
  • Backend: Scalability, reliability, consistency
  • Infrastructure: Resource management, multi-tenancy, isolation
  • ML: Model serving, feature stores, training pipelines
  • Frontend: Browser compatibility, rendering performance, state management

3. Resource Management

There’s a high likelihood the question will involve resource management

Key Areas to Mention:

  • Data Structures: What would the data structures look like?
  • Performance Trade-offs: Are there any performance trade-offs?
  • Define Data Structures: Define data structures (if applicable)
  • Scalability: How well would it scale on different architectures and performance envelopes?

Considerations:

  • Memory usage and optimization
  • CPU utilization
  • I/O bandwidth (disk, network)
  • Storage requirements
  • Power consumption (for mobile/embedded)
  • Network bandwidth
  • Cost implications

Domain-Specific Resource Management:

  • Mobile: Battery, memory, CPU, network data usage
  • Backend: Server resources, database connections, cache
  • Infrastructure: Compute, storage, network resources
  • ML: GPU/TPU resources, model storage, inference compute

4. Tradeoffs

Discuss pros/cons and make informed choices

Key Actions:

  • After discussing the pros/cons of each option, choose 1-2 methods to design a solution
  • Explain why you’re making this choice and how it is right for the domain/service
  • Make your tradeoff clear

Common Trade-offs in System Design:

  • Performance vs. Memory usage
  • Latency vs. Throughput
  • Consistency vs. Availability (CAP theorem)
  • Complexity vs. Maintainability
  • Cost vs. Performance
  • Security vs. Usability
  • Synchronous vs. Asynchronous operations

Domain-Specific Trade-offs:

  • Mobile: Battery life vs. Performance, Offline vs. Online
  • Backend: Strong consistency vs. Eventual consistency
  • Infrastructure: Isolation vs. Resource efficiency
  • ML: Model accuracy vs. Inference latency, Training time vs. Model size

5. Deep Dive

Showcase your expertise in a specific area

Key Actions:

  • Do a deep dive on some facet of the problem you feel comfortable with
  • This is where you can highlight your expertise in any one area
  • You will be assessed on your depth in “X” area
  • Discuss any additional concerns, how to prevent them, and your approach
  • It’s okay to go back and forth with your tradeoff if later you realize there is a more efficient method

Deep Dive Areas (Domain-Specific):

  • Mobile:
    • Specific Android/iOS framework APIs
    • Memory management and lifecycle
    • Performance optimization techniques
    • Native code integration
  • Backend:
    • Database design and optimization
    • Caching strategies
    • Distributed system patterns
    • API design
  • Infrastructure:
    • Container orchestration
    • Service mesh
    • Monitoring and observability
    • Resource scheduling
  • ML:
    • Model architecture
    • Feature engineering
    • Training infrastructure
    • Model serving optimization

6. Quantitative Analysis

Think quantitatively about your design

Key Actions:

  • This can be done towards the end or in between discussions
  • Try to think quantitatively about how your design will work in reality
  • Make some approximate calculations

Calculations to Consider:

  • Throughput: Requests per second, operations per second, transactions per second
  • Latency: Average latency, p50, p95, p99 latency
  • Memory: Total memory usage, per-request memory, memory footprint
  • Storage: Data size, storage requirements, growth rate
  • Bandwidth: Network bandwidth requirements, data transfer rates
  • Capacity: How many users/devices can be supported
  • Cost: Resource costs, infrastructure costs

Example Calculations:

  • If we have X users, each making Y requests per second, what’s our total QPS?
  • If each request processes N bytes of data, what’s our memory footprint?
  • How many servers/instances do we need for the target load?
  • What’s the storage requirement for X days of data retention?
  • What’s the network bandwidth needed for real-time updates?

Common Meta In-Domain Design Questions

Meta-Specific Questions

  1. Design Facebook News Feed
    • Display updates from friends and followed pages
    • Real-time feed generation and ranking
    • Handle billions of users and posts
    • Efficient data retrieval and caching
  2. Design Facebook Status Search
    • Search posts, statuses, videos from friends and followed pages
    • Indexing massive amounts of content
    • Real-time search updates
    • Ranking and relevance algorithms
  3. Design Live Commenting
    • Real-time commenting on Facebook posts
    • Users see new comments as they appear
    • Handle concurrent comments from multiple users
    • WebSocket or polling mechanisms
  4. Design Facebook Messenger / WhatsApp
    • 1:1 and group conversations
    • Real-time message delivery
    • Track online/offline status
    • Message persistence and ordering
    • End-to-end encryption considerations
  5. Design Instagram
    • Upload and share photos/videos
    • Follow other users
    • Like photos
    • Scrollable feed of photos from followed users
    • Media storage and CDN distribution
  6. Design Proximity Server
    • Discover nearby places and events
    • Query nearby places within a given distance
    • Geospatial indexing (GeoHash, R-trees)
    • Time-based queries
  7. Design Privacy Settings
    • Specify privacy levels for posts (Public, Friends, etc.)
    • Efficient privacy checks
    • Access control enforcement
    • Querying posts with privacy constraints
  8. Design Typeahead Suggestions
    • Autocomplete search queries
    • Suggest top 10 queries based on typed characters
    • Efficient prefix matching (Trie data structures)
    • Real-time suggestions

Domain-Specific Preparation

Mobile (Android/iOS)

Key Topics:

  • Platform-specific frameworks and APIs
  • Memory management (Android lifecycle, iOS memory management)
  • Network optimization and data usage
  • Battery optimization
  • Native code integration (JNI, Swift/C interop)
  • UI/UX considerations
  • Offline functionality
  • Push notifications
  • Background processing

Common Design Questions:

  • Design a local caching system for offline support
  • Design a background sync mechanism
  • Design a push notification system
  • Design a media upload/download system
  • Design an offline-first mobile app
  • Design a location tracking system for mobile
  • Design a mobile analytics system
  • Design a mobile crash reporting system

Backend Systems

Key Topics:

  • Distributed systems architecture
  • Database design (SQL, NoSQL)
  • Caching strategies
  • API design (REST, GraphQL, gRPC)
  • Message queues and event streaming
  • Microservices architecture
  • Scalability patterns
  • Consistency models

Common Design Questions:

  • Design a distributed cache
  • Design a notification system
  • Design a real-time feed system
  • Design an API gateway
  • Design a message queue system
  • Design a distributed lock service
  • Design a service discovery system
  • Design a rate limiter
  • Design a URL shortener

Infrastructure/Platform

Key Topics:

  • Container orchestration (Kubernetes)
  • Service mesh
  • CI/CD pipelines
  • Monitoring and observability
  • Resource management
  • Multi-tenancy
  • Security and isolation
  • Configuration management

Common Design Questions:

  • Design a container orchestration system
  • Design a service discovery system
  • Design a monitoring system
  • Design a configuration management system
  • Design a load balancer
  • Design a distributed tracing system
  • Design a logging aggregation system
  • Design a secrets management system

Machine Learning

Key Topics:

  • Model serving infrastructure
  • Feature stores
  • Training pipelines
  • Model versioning
  • A/B testing infrastructure
  • Real-time inference
  • Model monitoring
  • Data pipelines

Common Design Questions:

  • Design a model serving system
  • Design a feature store
  • Design a training pipeline
  • Design an A/B testing system
  • Design a recommendation system
  • Design a real-time prediction system
  • Design a model versioning system

Frontend

Key Topics:

  • Browser rendering and performance
  • State management
  • Component architecture
  • Build and bundling systems
  • Progressive Web Apps
  • Browser APIs
  • Cross-browser compatibility
  • Asset optimization

Common Design Questions:

  • Design a state management system
  • Design a build/bundling system
  • Design a component library
  • Design a real-time update system
  • Design a progressive web app
  • Design a client-side caching system

Interview Preparation Checklist

General Preparation

  • Review the 6 evaluation areas (Problem Exploration, Design Approach, Resource Management, Tradeoffs, Deep Dive, Quantitative Analysis)
  • Practice asking clarifying questions
  • Prepare examples of previous projects in your domain
  • Review common design patterns in your domain
  • Practice drawing architecture diagrams
  • Practice quantitative analysis (calculations, estimates)
  • Review trade-offs common in your domain

Domain-Specific Preparation

  • Review core domain topics and frameworks
  • Understand domain-specific constraints and considerations
  • Review domain-specific design patterns
  • Practice domain-specific design questions
  • Review recent developments in your domain
  • Understand domain-specific scalability challenges

Communication Preparation

  • Practice explaining your thought process clearly
  • Practice discussing trade-offs
  • Prepare to adjust your approach based on feedback
  • Practice drawing diagrams while explaining
  • Prepare to ask follow-up questions

Common Pitfalls to Avoid

1. Jumping to Solutions Too Quickly

Don’t: Start designing immediately without understanding the problem Do: Ask clarifying questions first, understand requirements, then design

2. Not Asking Questions

Don’t: Assume you understand everything about the problem Do: Ask questions about scale, constraints, requirements, and success criteria

3. Ignoring Trade-offs

Don’t: Present one solution without discussing alternatives Do: Discuss multiple approaches and their trade-offs before choosing

4. Not Showing Depth

Don’t: Stay at a high level throughout the interview Do: Deep dive into at least one area to show your expertise

5. Not Thinking Quantitatively

Don’t: Design without considering numbers (scale, performance, capacity) Do: Make calculations and estimates to validate your design

6. Being Inflexible

Don’t: Stick to your initial design even when feedback suggests changes Do: Be willing to adjust your approach based on interviewer feedback

Key Takeaways

  1. Domain Expertise Matters: Use your deep knowledge in your domain to inform your design
  2. Ask Questions: Don’t hesitate to clarify requirements and constraints
  3. Show Thought Process: Explain your reasoning at each step
  4. Discuss Trade-offs: Always consider pros and cons of different approaches
  5. Demonstrate Depth: Deep dive into areas you’re comfortable with
  6. Think Quantitatively: Make calculations and estimates to validate your design
  7. Be Collaborative: Engage with the interviewer and adjust based on feedback
  8. Communication is Key: Clear communication is as important as technical knowledge

Conclusion

Preparing for Meta’s In-Domain Design interview requires a combination of:

  • Deep Domain Knowledge: Strong understanding of your specific domain
  • Design Skills: Ability to design systems/components in your domain
  • Problem-Solving Skills: Using domain knowledge to solve ambiguous problems
  • Communication Skills: Clearly explaining your thought process
  • Trade-off Analysis: Understanding and discussing design trade-offs

Remember: The interview is designed to assess your ability to design systems in your domain, not to test your memory of specific APIs or implementations. Focus on demonstrating your thought process, asking the right questions, and showing how you approach complex problems using your domain expertise.

The 6 evaluation areas (Problem Exploration, Design Approach, Resource Management, Tradeoffs, Deep Dive, and Quantitative Analysis) provide a framework for structuring your interview responses. Use them to guide your preparation and during the interview itself.

Good luck with your Meta In-Domain Design interview preparation!