Meta Interview Guide: Cracking with AI

A comprehensive interview guide incorporating AI for problem-solving!

💡TIP: Getting a shortlisting call from Meta is hard. The chances are 10%. However, referrals work very well for both internships and full-time roles. I would highly recommend applying via referrals.

The following is the interview structure once you get the interview call and how it will be transformed with AI:

📞 Step 1: Recruiter Call

• Duration: 20-30min

• Focus: A brief conversation covering your career background and interest in Meta.

• Typical Questions:

Tell me about yourself
Why Meta?
Walk me through your resume
Discussion of interview process and timeline

• 🔥 AI-based Update: This step will not have AI, as there is no right or wrong answer to these questions, it is completely based on your prior experience and how well you articulate your thoughts. However, Meta recruiters may use AI-powered tools behind the scenes for:

  • Automated scheduling and calendar optimization

  • Candidate profile analysis to identify key talking points

  • Real-time note-taking assistance during calls

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💻 Step 2: Technical Phone Screen(s)

• Duration: 45min each (can have 1-2 rounds)

• Focus: DSA and coding under pressure: Conducted via CoderPad-like platforms and hence no syntax autocomplete available.

• Typical Questions:

Two Sum / Three Sum variations
Valid Parentheses
Merge Two Sorted Lists
Binary Tree Traversal
Array rotation problems

• 🔥 AI-based Format: You should consider AI as an entry-level engineer and be able to supervise and validate AI-generated code rather than merely writing a leetcode solution.
  

  Example Evolution:

  • Traditional: "Implement Binary Search from scratch"

  • AI-Assisted: Use AI to implement Binary Search, then modify it for a rotated sorted array. Explain your prompting strategy and validate the AI's edge case handling. Explain the choice of data structure and time and space complexity for the solution.

  New Skills Being Tested:

  • Prompt Engineering: How effectively you communicate with AI

  • Code Review: Spotting errors in AI-generated code and adding edge cases

  • Iterative Refinement: Improving AI solutions through better prompts

  • AI Limitation Recognition: Knowing when to override AI suggestions

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Step 3: Onsite or “Loop” Interviews

Coding Interviews (2–3 rounds)

• Duration: 45min each

• Focus: LeetCode medium to hard problems in data structures & algorithms.

• Typical Questions:

Array manipulation (Eg: "Merge Intervals")
Tree/Graph traversal (Eg: "Binary Tree Level Order", "Cycle Detection")
Dynamic Programming (Eg: "Longest Increasing Subsequence")
String manipulation (Eg: "Valid Parentheses")
LCA (Lowest Common Ancestor)

🔥AI-based Update:

• Example 1: AI-based Coding

  • Traditional Approach: "Implement a LRU Cache" where Candidate codes from scratch without AI, based on a leetcode solution.

  • AI-Based approach: "Build a scalable caching system for Meta's news feed". Use AI to generate initial implementation and then modify code for edge cases. Explain the code in detail. Justify the choice of data structures and also optimize time and space complexity if required.

• Example 2: AI Debugging & Optimization

  • Scenario: Given a buggy AI-generated code for a complex algorithm, identify issues, fix bugs, and optimize performance. This will test your debugging, problem-solving and critical thinking skills rather than merely relying on AI solutions.

• Example 3: Advanced AI Integration

  • Problem: "Implement a content recommendation engine"

  • Challenge: AI provides multiple conflicting solutions

  • Assessment: Your decision-making process in choosing the best AI-suggested approach by explaining the design choices and optimizing time & space complexity.

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System Design Interviews (1–2 rounds)

• Duration: 45min each

• Focus: Low-level and high-level design questions

  • Scalability and load balancing

  • APIs interfaces

  • Handling data consistency across multiple servers

EXAMPLES:
Design a messaging system like Facebook Messenger
Design Ad impressions aggregator
Design Facebook News Feed
Design Instagram
Design WhatsApp messaging system
Design notification system
Design the backend for a chat application

🔥AI-based Update:

• Step 1: Prompt AI for initial system architecture, make modifications if required and explain your reasoning for accepting/rejecting AI proposals.

• Step 2: Use AI to explore specific components (database design, API structure, caching techniques) and also test all possible edge cases.

• Step 3: Identify areas where AI fell short and explain human insight that AI missed.

New Example Questions:

Use AI to design Instagram's story feature, but explain where you disagree with its suggestions

Collaborate with AI to build a distributed caching system, highlighting AI's blind spots

Design a real-time chat system using AI assistance, then optimize for Meta's scale requirements

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🗣 Behavioral Interview (1 round)

• Duration: 45min

• Focus:

  • Meta’s core values: Be Bold, Focus on Impact, Move Fast, Be Open, Build Social Value.

  • Questions about leadership, conflict resolution, cross-functional collaboration, etc using STAR method.

META'S CORE VALUES:

"Move Fast":Tell me about a time you had to make a quick decision
"Focus on Impact":Describe a project where you drove significant results
"Be Open":Share an example of receiving and acting on feedback
"Build Social Value": How have you helped others succeed?

BEHAVIORAL QUESTIONS:

"Tell me about a time you disagreed with your manager"
"Describe a challenging project you led"
"How do you handle competing priorities?"
"Tell me about a time you failed and what you learned"
“Tell me about a time you took a bold risk.”
“Describe a cross-functional conflict and how you resolved it.”
“Tell me about a time when your team was demotivated—what did you do?”

🔥AI-based Update: Expect scenarios and questions that assess how candidates interact with AI tools, focusing on whether they can leverage AI effectively and responsibly:

• "How do you maintain code quality when using AI assistance?"

• "Describe your process for validating AI-generated code in production"

• "Share an example where AI helped you solve a problem you couldn't solve alone"

• "Tell me about a time you had to explain AI-generated code to a non-technical stakeholder"

• "How do you balance AI efficiency with team learning and development?"

• "Describe a situation where over-reliance on AI caused problems in your team"

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🏆 Step 4: Decision Making

• Hiring Committee Review

  • Collective evaluation of all interviewer feedback to make the final hiring decision. Meta emphasizes that human interaction remains central to hiring decisions. AI will simply augment and not replace interviewers.

• Team Matching

  • For successful candidates, Meta may coordinate placement into a fitting team via manager calls.

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📌 Final Tips

To crack Meta:

• Apply through a referral to boost your chances.

• Master DSA with consistent LeetCode practice.

• Master effective prompting techniques and the ability to review and debug AI-generated code.

• Learn Meta’s core values and prepare STAR-based stories from your experience.

• Strengthen your System Design skills, especially around infrastructures of apps like WhatsApp and Instagram.

Thanks for reading!
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